This paper describes a general framework for transforming a sequential program into a network of processes , which are then converted to hardware accelerators through high level synthesis . Also proposed is a complementing technique for performing static deadlock analysis of the generated accelerator network . FPGAs have been rapidly adopted for acceleration of Deep Neural Networks ( DNNs ) with improved latency and energy efficiency compared to CPU and GPU-based implementations . High-level synthesis ( HLS ) is an effective design flow for DNNs due to improved productivity , debugging , and design space exploration ability . However , optimizing large neural networks under resource constraints for FPGAs is still a key challenge . In this paper , we accelerate the proposed algorithm with simultaneous reconstruction and segmentation using the Mumford-Shah model by FPGA devices . The algorithm is hand-optimized with both algorithmic domain knowledge and platform-specific information before translated into FPGA implementation using high-level synthesis and other electronic system-level design tools . A high-level performance model is used to guide the design and optimization process at early stages . Power consumption could easily be computed as the sum of the power consumptions of individual chips , and hot chips could be cooled with a heatsink . Partitioning onto multiple boards was performed manually , or at least handled separately from the high-level synthesis process . General-purpose multiprocessors were constructed only in research laboratories and special-purpose multiprocessors were expensive and constructed for only a few applications . Estimation of resources at various stages of the high-level synthesis process is essential to guide high-level synthesis algorithms towards optimal solutions . Lower bound es timation bounds the design space and gives an indication of the quality of the design synthesized . The role of partitioning is to discover the structure implicit in the functional specification of the system so as to guide high level synthesis decisions in a design environment for digital systems consisting of hardware parts and possibly software components . The partitioning algorithm can also be used to partition the final or intermediate results of a high-level synthesis process into several physical blocks . Experimental results show that the approach produces better register-transfer designs with less global communications.<<ETX > > RVC-CAL is a dataflow programming language that permits design abstraction , modularity , and portability . The objective of this thesis is to provide a high-level synthesis solution for RVC-CAL dataflow programs and provide an RVC-CAL design flow for heterogeneous platforms . The main contributions of this thesis are : a high-level synthesis infrastructure that supports the full specification of RVC-CAL , an action selection strategy for supporting parallel read and writes of list of tokens in hardware synthesis , a dynamic fine-grain profiling for synthesized dataflow programs , an iterative design space exploration framework that permits the performance estimation , analysis , and optimization of heterogeneous platforms , and finally a clock gating strategy that reduces the dynamic power consumption . Furthermore , our framework can automatically generate the optimized communication blocks for fine-grained communication between hardware blocks . Experimental evaluation demonstrates that we can achieve an average of 6.04X speedup over the high level synthesis solution without our transformations to enable intra- and inter-block optimizations . Moreover , when facing real data , the speed will further increase by nearly 100 % . To increase designer productivity , we reinforced traditional HDL-based hardware design flow with High-Level Synthesis concept . The prototyping system on a customized FPGA-based accelerator card can operate in 200MHz stably . The development of next-generation computer-aided design tools and field reconfigurable architectures require efficient high-level data-flow synthesis . In this paper , we present the transform rules from polynomial presentation to polynomial matrix presentation and show how symbolic algebra can be used to construct arithmetic-level decomposition algorithm with extensibility that leads to efficient high-level synthesis of system . And the simple verification approach for reconfigurable architectures is given . This paper points out that register binding in the high-level synthesis stage has a significant impact on the clocking constraints between registers . As a result , different register binding solutions often lead to different smallest feasible clock periods . Abstract : The Multicomponent Synthesis System ( MSS ) is a vertically integrated collection of design synthesis tools for top-down design of multichip modules ( MCMs ) from behavioral specifications . MSS consists of a high-level synthesis subsystem , a partitioning engine , a collection of structural silicon compilers , a test compiler , a test bench compiler , a package compiler , a performance specification and verification subsystem , and various component libraries and technology files . MSS is a VHDL-centered design environment . Moreover , the relations can be efficiently found using a heuristic timing analysis algorithm . By allowing significantly larger designs to be synthesized , this work is a step towards the development of high-level synthesis tools for system level asynchronous circuits . This creates a significant barrier for design optimization since even experts typically need to test many designs due to the non-obvious interactions between the different optimizations . Thus , understanding the design space , and guiding the optimization process is a crucial requirement for enabling the widespread adoption of these high-level synthesis tools . However this requires a significant amount of design space data that is currently unavailable or difficult to generate . FPGAs are used in implementation of these filters . The two design processes which are involved in the implementation are namely , HDL and a high level synthesis design Tool . In effect they can provide with efficient all-software design flow from high-level languages , as well as higher performance and lower power consumption than general purpose processors . As an illustration , this paper introduces a dynamic reconfigurable processor along with a high-level synthesis design tool . The biennial mini-symposium “ Parallel computing with FPGAs ” brings together research on applications and tools fostering the use of field programmable gate arrays . Key aspects are the efficiency , programmability , scalability and portability of high-level synthesis languages and tools . In particular this year 's contributions present productivity and programmability results of using HLS languages OpenCL , OmpSs , MATLAB/Octave , OpenSPL and Vivado HLS . Hence ' SAYEH ' is a simple architecture , yet enough hardware that has been designed for educational and benchmarking purposes , and also can be used for real embedded processor applications . Despite strides in high-level synthesis tools and techniques , implementing processer remains a complex task . Processors play a major role in the design of embedded systems . This service is composed by a hardware platform where a Design Under Test ( DUT ) is deployed into a dynamically reconfigurable area . DUTs are generated using High-Level Synthesis ( HLS ) tools , and are verified through unit testing , checking its behavioural and timing correctness . These tests are the same at any abstraction level . Today 's high-level synthesis ( HLS ) tools rely on static scheduling . When control and/or data dependencies that can not be resolved at compile time are present in program code , HLS tools produce pessimistic schedules based on worst-case assumptions , which may not be the common case or may never even occur in practice . However , the FPGA-based simulation system requires handcrafted hardware description language ( HDL ) and significant design efforts . This paper explores FPGA-based EMT real-time simulation system design using high-level synthesis ( HLS ) technology to accelerate the design process and improve the flexibility , because high-level language can be transferred into HDL using HLS . Furthermore , loop unroll and pipeline optimisation directives are analysed and applied for HLS to improve the computation speed of the components . A new HW/SW co-design skin segmentation step to locate the eyes/face is proposed . Our presented new approach migrates the skin segmentation step from processing system ( SW ) to programmable logic ( HW ) IP taking the advantage of High Level Synthesis ( HLS ) tool flow to accelerate the design and prototyping flow of the vision based ADAS on a hardware Zynq platform . Buffering frequently reused data on chip is a common approach to address this problem and the optimization of the cache architecture introduces yet another complex design space . This paper presents a high-level synthesis ( HLS ) design aid that generates parallel application-specific multi-scratchpad architectures including on-chip caches . Our program analysis identifies non-overlapping memory regions , supported by private scratchpads , and regions which are shared by parallel units after parallelization and which are supported by coherent scratchpads and synchronization primitives . Modern high-level synthesis (HLS)-based tools allow for the creation of complex systems where parts of the user 's software are executed on a conventional processor , and the other parts are implemented as hardware accelerators via HLS flows . While modern tools allow designers to construct these systems relatively quickly , observing and debugging the real-time execution of these complex systems remains challenging . I describe this new IR – the Value State Flow Graph ( VSFG ) – and how it statically exposes ILP from control-flow intensive code by enabling control-dependence analysis , execution along multiple flows of control , as well as aggressive control-flow speculation . I also present a High-Level Synthesis ( HLS ) toolchain , that compiles unmodified high-level language code to dataflow custom hardware , via the LLVM compiler infrastructure . I show that for control-flow intensive code , VSFG-based custom hardware performance approaches , or even exceeds the performance of a complex superscalar processor , while consuming only 1/4 × the energy of an efficient in-order processor , and 1/8 × that of a complex out-of-order processor . LegUp [ 1 ] is an open-source high-level synthesis ( HLS ) tool that accepts a C program as input and automatically synthesizes it into a hybrid system . The hybrid system comprises an embedded processor and custom accelerators that realize user-designated compute-intensive parts of the program with improved throughput and energy efficiency . To effectively implement this adder we use the concept of " Hybrid Variable Latency " technique . The Performance parameters of the adder are reported using Cadence tool ( RTL-Compiler ) in 45-nm technology and functionality of the design is validated using Vivado HLS . In comparison with the existing techniques , the proposed scheme is relatively faster and also it shows improvement in delay by 7.19 % and 15.63 % respectively . From acommon source script , Sonar can generate testbenches writtenin SystemVerilog ( SV ) and C++ . These files can then beimported into standard simulation tools such as ModelSim[2 ] or Vivado HLS and run . The use of Python makes iteasy to extend Sonar with higher layers of abstraction fortestbenches and integrate it with other software platforms . However , the cost and difficulty of hardware design is often prohibitive , and consequently , a software approach is used for most applications . In this article , we introduce a new high-level synthesis tool called LegUp that allows software techniques to be used for hardware design . LegUp accepts a standard C program as input and automatically compiles the program to a hybrid architecture containing an FPGA-based MIPS soft processor and custom hardware accelerators that communicate through a standard bus interface . While the EDSL is not pure C , it provides a user-friendly language that can be trivially translated from a recursive C program , and can contain calls to plain C for compute-intensive leaf functions . HLSRecurse is a platform independent library that allows the same program to be compiled using g++ , clang , Legup , or Vivado HLS , and have the same execution semantics . The performance of HLSRecurse is evaluated in software and hardware using three micro-benchmarks of the underlying state-machine builder , and five practical examples of real recursive programs including a Sudoku solver , Strassen multiplication , and adaptive Monte-Carlo integration . LegUp is an open-source high-level synthesis framework that simplifies the hardware accelerator design process [ 8 ] . With LegUp , a designer can start from an embedded application running on a processor and incrementally migrate portions of the program to hardware accelerators implemented on an FPGA . The final application then executes on an automatically-generated software/hardware coprocessor system . This paper presents on overview of the LegUp design methodology and system architecture , and discusses ongoing work on profiling , hardware/software partitioning , hardware accelerator quality improvements , Pthreads/OpenMP support , visualization tools , and debugging support . In the context of parallel hardware , we investigate architectural and memory optimizations that help to improve circuit performance and area , and discuss a method of using the producer-consumer pattern in software to infer a streaming circuit in hardware . With these techniques , we show that LegUp can produce high-performance hardware that can be competitive to circuits that are generated by commercial HLS tools , and demonstrate that LegUp-generated circuits can also outperform software executing on x86 processors . LegUp supports the synthesis of most of the C language to hardware , including loops , structs , multi-dimensional arrays , pointer arithmetic , and floating point operations . The LegUp distribution includes the CHStone HLS benchmark suite , as well as a test suite and associated infrastructure for measuring quality of results , and for verifying the functionality of LegUp-generated circuits . LegUp is freely downloadable at www.legup.org , providing a powerful platform that can be leveraged for new high-level synthesis research . However , such standard cores can be over provisioned or lack specific specializations as required by applications . We consider FP cores described in the C language , synthesized to hardware using the LegUp high-level synthesis ( HLS ) tool [ 1 ] . Their software specification permits straightforward customization to non-compliant variants having superior area and performance characteristics , such as reduced-precision floating point , or cores without full IEEE 754 exceptions support . NoCs provide scalable connectivity between FPGA datapaths and memory banks and allow parallel routing of packets from datapaths to the banks . We develop a LegUp 5.0 compiler pass that ( 1 ) handles loops with indirect memory access by inserting NoCs into the circuit as required , ( 2 ) provides a performance and resource tuning framework for optimizing the resulting hardware , and ( 3 ) obviates the need for NoC expertise during programming . We quantify the effectiveness of our approach across a range of kernels with indirect accesses by comparing against baseline LegUp 5.0 targeting a Xilinx VC707 board . In addition , we show that we can support the pipelining of loops containing atomics by injecting further inter-iteration constraints . We implement our approach on two constraint-based scheduling HLS tools : LegUp 4.0 and LegUp 5.1 . We extend both tools to support two memory models that are capable of synthesising atomics correctly . For this experimental purpose , I defined benchmarks into DLP / HLS catalogues , which are " data " and " function " intensive respectively . The programs of DLP will be executed in LEON3 MP and LE1 CMP multi-processor systems and the programs of HLS in the LegUp Co-design system on target FPGAs . In preliminary , the performance of the soft-core processors will be examined by executing all the benchmarks . The hybrid system comprises an embedded processor and custom accelerators that realize user-designated compute-intensive parts of the program with improved throughput and energy efficiency . In this paper , we overview the LegUp framework and describe several recent developments : 1 ) support for an embedded ARM processor , as is available on Altera 's recently released SoC FPGA , 2 ) HLS support for software parallelization schemes -- pthreads and OpenMP , 3 ) enhancements to LegUp 's core HLS algorithms that raise the quality of the auto-generated hardware , and , 4 ) a preliminary debugging and verification framework providing C source-level debugging of HLS hardware . Since its first release in 2011 , LegUp has been downloaded over 1000 times by groups around the world , providing a powerful platform for new research in high-level synthesis algorithms and embedded systems design . By using HLS in a framework for architectural exploration of digital hardware , the number and diversity of architectural variations that can be generated and evaluated is far greater than what could have been done manually . During a previous project , the HLS-tool LegUp was explored . The goal was to see if the tool could be used the described framework . To gain insight on how to use FPGAs in high-performance computing , we created four different systems and evaluated them using benchmarks from the Rodinia benchmark suite . The systems we evaluated were diverse in both programming model and generality , and range from a custom-built 30-core manycore system to FSM-based accelerators using LegUP and deep data-flow pipelines using Intel FPGA SDK for OpenCL . We found that the original version of LegUp does not achieve very good performance out of the box ; still , with some non-trivial modification in the architecture , we improved its performance by up to 10 times . In this paper , the design details of square root module are discussed from the viewpoint of resource and speed optimization . For a n-bit number , with an appropriate algorithm and high-level synthesis design method , it can be computed only by using 2n+ log2n -3 bits Registers and a Comparator in n/2 clocks . We believe that programmability of future heterogeneous computing platforms has brought a new urgency to bear on several old problems in high-level synthesis for FPGAs . Our focus is on the two areas we believe are most underdeveloped in today ’s high-level synthesis software : eective utilisation of the numerical exibility aorded by high-level correctness specications , and application-specic memory subsystem synthesis . We conclude with our perspective on the likely future evolution of the eld . We believe that programmability of future heterogeneous computing platforms has brought a new urgency to bear on several old problems in high-level synthesis for FPGAs . Our focus is on the two areas we believe are most underdeveloped in today ’s high-level synthesis software : effective utilisation of the numerical flexibility afforded by high-level correctness specifications , and application-specific memory subsystem synthesis . We conclude with our perspective on the likely future evolution of the field . Along with the development of VLSI technology and the trend of system-on-chip design , traditional high-level synthesis can not deal with relatively complexity of system-on-chip design . In order to achieve optimal resource allocation , meet its performance and power requirements , and reduce its design time , we need a high-level synthesis software dealing system-level behavior . In consideration of system complexity , we have proposed a high-level synthesis method that synthesis for the task-level grains in a system behavior . Another advantage of our technique is that CLB boundaries in the final design are aligned to RTL module boundaries providing modularity and ease in testing . Thus this technique is very suitable for integration as a technology mapping phase with a high-level synthesis package . A digital module for the relay station is designed in order to relay data to and from the BISC chip . The digital module is written in SystemC and synthesized for FPGA fabric using a high- level synthesis tool . The module successfully interfaces to the PCB and is capable of relaying 83.2 Mbit/s of data from the BISC chip to memory . We compare the results obtained with the execution of the same benchmarks written in OpenCL , in the same architectures . The results show that OMPSs greatly outperforms the OpenCL environment . It is more flexible to exploit multiple accelerators . The resulting scheme exhibits interesting conservation and stability properties . For achieving high performance , the scheme is tested on recent multi-core processors and GPUs , using the OpenCL environment . The resulting scheme exhibits interesting conservation and stability properties . For achieving high performance , the scheme is tested on recent muulticore processors and GPU , using the OpenCL environment . This work introduces CLBlast , an open-source BLAS library providing optimized OpenCL routines to accelerate dense linear algebra for a wide variety of devices . It is targeted at machine learning and HPC applications and thus provides a fast matrix-multiplication routine ( GEMM ) to accelerate the core of many applications ( e.g. deep learning , iterative solvers , astrophysics , computational fluid dynamics , quantum chemistry ) . Our experiments indicate that the MPI-CUDA codes based on FDM and FEM achieves nearly 6x speed-ups for large mesh sizes in comparison to host-cpu implementation of the same code . The un-optimized OpenCL implementation GPU times have shown marginal improvement in speed-ups whereas counterpart the CUDA codes achieved maximum speedup of 4x to 6x on HPC GPU Cluster . We presented performance analysis for different mesh sizes that prove performance capabilities of performance and scalability of FDM and FEM computations GPU cluster . On the other hand , providing high performance in the presence of irregular memory access patterns to off-chip memory remains a challenge for the automated synthesis flows . In this paper , we study the performance/energy characteristics of OpenCL-generated FPGA designs on irregular memory access patterns , targeting XSBench , a memory-intensive Monte Carlo simulation code , as a case study . To complete our study , we implement XSBench in OpenCL and study optimization strategies for FPGAs . While there are benchmarks for measuring the sustained memory bandwidth for CPUs and GPUs , such a benchmark for FPGAs has been missing . We present MP-STREAM , an OpenCL-based synthetic micro-benchmark for measuring sustained memory bandwidth , optimized for FPGAs , but which can be used on multiple platforms . Our main contribution is the introduction of various generic as well as device-specific parameters that affect memory bandwidth . In particular , we will cover architectural features of FPGAs that make them well suited to many complex operations , including matrix multiplications and convolutions . In addition , we will introduce you to programming FPGAs using the Intel FPGA SDK for OpenCL ™ , and how specific OpenCL coding techniques can lead to efficient circuits implemented on the FPGA . Finally , we will go over several case studies where FPGAs have shown very competitive performance when programmed using OpenCL , including convolutional neural nets , FFTs , and astronomy de-dispersion algorithms . In this work , FFTS has been modified to dynamically exploit streaming store instructions on x86 machines , resulting in speedups of over 10 % , Also , when dynamic code generation is prohibited on some mobile platforms , FFTS has been altered to avoid it , while maximizing the performance . In paper Computer Vision Accelerators for Mobile Systems based on OpenCL GPGPU Co-Processing , Wang , Xiong , W. J. Gross ( * ) Department of Electrical and Computer Engineering , McGill University , Montreal , Quebec , Canada e-mail : warren.gross@mcgill.ca However , FPGAs are capable of far more than acceleration , hence it is interesting to explore if OpenCL can be used to take advantage of FPGAs for more generic applications . To answer these questions , especially in the context of High Energy Physics , two applications , a DAQ module and an acceleration workload , were tested for implementation with OpenCL on FPGAs2 . The challenges on using OpenCL for a DAQ application and their solutions , together with the performance of the OpenCL based acceleration are discussed . Then , we provide the schedulability test through rigorous theoretical analysis . We conduct extensive simulations , and the results demonstrate that the proposed scheduling algorithm and analysis significantly outperform existing ones . A system framework was set up combining advantages of distributed scheduling and centralized scheduling based on multi-agents.,The framework included a centralized part and a multi-agent scheduling net . The former adopted optimized Genetic Algorithm(GA ) to select diversified scheduling schemes , and the latter consisted of resource agent and cooperation protocol to keep well adaptation to dynamic manufacturing environment . Priority of each order was coded as a gene in a chromosome with each chromosome representing a scheduling scheme . Sequence processing time was arranged by multi-agent scheduling net according to order priority and scheduling protocol . Then the fitness value was calculated based on the Early/Tardiness value of each order obtained through multi-agent scheduling net . Based on this calculation , an optimized scheduling scheme was selected . We propose a playback length changeable 3D video data chunk segmentation algorithm . A novel hybrid-priority based 3D video P2P data scheduling algorithm is presented . Bandwidth-adaptive 3D video P2P streaming for heterogeneous networks is implemented . A terminal to provide a method for receiving a downlink data channel in a wireless communication system . A method according to the invention , step a , first schedule information for the terminal specific reference signal based downlink data channel for receiving information about one or more settings that define a large characteristic of the UE-specific reference signal and detecting any one of the second schedule information , wherein the receiving terminal specific reference signal based downlink data channel , the first schedule information , the one of the one or more settings includes an indicator for indicating a large characteristic defined is defined by the set indicated by indicator during the first schedule information detected at the second scheduling information detection , by setting already set out the one or more settings is the fact characterized . .The These results motivate the development and assist the evaluation of two polynomial-time heuristic algorithms for 2-processor scheduling with limited memory . For constant degree graphs it is shown that the heuristics can be use¬ ful scheduling tools . It is also shown that use of the inclusive-cuts graph can lead to an improvement in perform¬ ance , but at the expense of additional scheduling overhead . Fatalities require the execution of close out procedures , which take a specified period of time ( and must be given precedence over living patients ) . Through simulation we compare the current defacto scheduling processes in use within CCUs , against a new scheduling algorithm that makes use of an integrated array of patient information collected by a hypothetical vital sign integration infrastructure . Our simulation study provides quantitative evidence from which we can measure the extent to which such an infrastructure reduces risk to CCU patients and lowers operational personnel costs . Spiral allows a stretch of O(log(n)2 log(D ) ) where n is the number of nodes in the network and D the diameter of the network . Finally the third parallel was between oblivious routing in networks and load balance transactional scheduling in NUMA . Jean-Philippe Martin presented his joint work with Christopher J. Rossbach on a model that predicts the performance for distributed software transactional memory ( DSTM ) executing a given workload . This would bring the load-unbalancing problem when the topology run failed and is killed by its user , or more new machines are extended in Storm cluster . Aiming at solving them , we design the S-Storm , a slot-aware scheduling strategy for even scheduler in Storm , which achieves a fine-grained EvenScheduler using the slot-aware sorting queue and merger factor . S-Storm has the following desirable features : 1 ) It evenly allocates slots for multi-topologies in a load-balancing cluster . In Commercial grids , users are regularly engaged with the distributed resource providers with whom they have little or no prior experience . The interaction between these distributed resource providers requires resource management and scheduling solutions . The allocation and scheduling of applications on a set of heterogeneous , dynamically changing resources is a serious problem .
This paper describes a general framework for transforming a sequential program into a network of processes , which are then converted to hardware accelerators through high level synthesis . Also proposed is a complementing technique for performing static deadlock analysis of the generated accelerator network .
FPGAs have been rapidly adopted for acceleration of Deep Neural Networks ( DNNs ) with improved latency and energy efficiency compared to CPU and GPU-based implementations . High-level synthesis ( HLS ) is an effective design flow for DNNs due to improved productivity , debugging , and design space exploration ability . However , optimizing large neural networks under resource constraints for FPGAs is still a key challenge .
In this paper , we accelerate the proposed algorithm with simultaneous reconstruction and segmentation using the Mumford-Shah model by FPGA devices . The algorithm is hand-optimized with both algorithmic domain knowledge and platform-specific information before translated into FPGA implementation using high-level synthesis and other electronic system-level design tools . A high-level performance model is used to guide the design and optimization process at early stages .
Power consumption could easily be computed as the sum of the power consumptions of individual chips , and hot chips could be cooled with a heatsink . Partitioning onto multiple boards was performed manually , or at least handled separately from the high-level synthesis process . General-purpose multiprocessors were constructed only in research laboratories and special-purpose multiprocessors were expensive and constructed for only a few applications .
Estimation of resources at various stages of the high-level synthesis process is essential to guide high-level synthesis algorithms towards optimal solutions . Lower bound es timation bounds the design space and gives an indication of the quality of the design synthesized .
The role of partitioning is to discover the structure implicit in the functional specification of the system so as to guide high level synthesis decisions in a design environment for digital systems consisting of hardware parts and possibly software components . The partitioning algorithm can also be used to partition the final or intermediate results of a high-level synthesis process into several physical blocks . Experimental results show that the approach produces better register-transfer designs with less global communications.<<ETX > >
RVC-CAL is a dataflow programming language that permits design abstraction , modularity , and portability . The objective of this thesis is to provide a high-level synthesis solution for RVC-CAL dataflow programs and provide an RVC-CAL design flow for heterogeneous platforms . The main contributions of this thesis are : a high-level synthesis infrastructure that supports the full specification of RVC-CAL , an action selection strategy for supporting parallel read and writes of list of tokens in hardware synthesis , a dynamic fine-grain profiling for synthesized dataflow programs , an iterative design space exploration framework that permits the performance estimation , analysis , and optimization of heterogeneous platforms , and finally a clock gating strategy that reduces the dynamic power consumption .
Furthermore , our framework can automatically generate the optimized communication blocks for fine-grained communication between hardware blocks . Experimental evaluation demonstrates that we can achieve an average of 6.04X speedup over the high level synthesis solution without our transformations to enable intra- and inter-block optimizations .
Moreover , when facing real data , the speed will further increase by nearly 100 % . To increase designer productivity , we reinforced traditional HDL-based hardware design flow with High-Level Synthesis concept . The prototyping system on a customized FPGA-based accelerator card can operate in 200MHz stably .
The development of next-generation computer-aided design tools and field reconfigurable architectures require efficient high-level data-flow synthesis . In this paper , we present the transform rules from polynomial presentation to polynomial matrix presentation and show how symbolic algebra can be used to construct arithmetic-level decomposition algorithm with extensibility that leads to efficient high-level synthesis of system . And the simple verification approach for reconfigurable architectures is given .
This paper points out that register binding in the high-level synthesis stage has a significant impact on the clocking constraints between registers . As a result , different register binding solutions often lead to different smallest feasible clock periods .
Abstract : The Multicomponent Synthesis System ( MSS ) is a vertically integrated collection of design synthesis tools for top-down design of multichip modules ( MCMs ) from behavioral specifications . MSS consists of a high-level synthesis subsystem , a partitioning engine , a collection of structural silicon compilers , a test compiler , a test bench compiler , a package compiler , a performance specification and verification subsystem , and various component libraries and technology files . MSS is a VHDL-centered design environment .
Moreover , the relations can be efficiently found using a heuristic timing analysis algorithm . By allowing significantly larger designs to be synthesized , this work is a step towards the development of high-level synthesis tools for system level asynchronous circuits .
This creates a significant barrier for design optimization since even experts typically need to test many designs due to the non-obvious interactions between the different optimizations . Thus , understanding the design space , and guiding the optimization process is a crucial requirement for enabling the widespread adoption of these high-level synthesis tools . However this requires a significant amount of design space data that is currently unavailable or difficult to generate .
FPGAs are used in implementation of these filters . The two design processes which are involved in the implementation are namely , HDL and a high level synthesis design Tool .
In effect they can provide with efficient all-software design flow from high-level languages , as well as higher performance and lower power consumption than general purpose processors . As an illustration , this paper introduces a dynamic reconfigurable processor along with a high-level synthesis design tool .
The biennial mini-symposium “ Parallel computing with FPGAs ” brings together research on applications and tools fostering the use of field programmable gate arrays . Key aspects are the efficiency , programmability , scalability and portability of high-level synthesis languages and tools . In particular this year 's contributions present productivity and programmability results of using HLS languages OpenCL , OmpSs , MATLAB/Octave , OpenSPL and Vivado HLS .
Hence ' SAYEH ' is a simple architecture , yet enough hardware that has been designed for educational and benchmarking purposes , and also can be used for real embedded processor applications . Despite strides in high-level synthesis tools and techniques , implementing processer remains a complex task . Processors play a major role in the design of embedded systems .
This service is composed by a hardware platform where a Design Under Test ( DUT ) is deployed into a dynamically reconfigurable area . DUTs are generated using High-Level Synthesis ( HLS ) tools , and are verified through unit testing , checking its behavioural and timing correctness . These tests are the same at any abstraction level .
Today 's high-level synthesis ( HLS ) tools rely on static scheduling . When control and/or data dependencies that can not be resolved at compile time are present in program code , HLS tools produce pessimistic schedules based on worst-case assumptions , which may not be the common case or may never even occur in practice .
However , the FPGA-based simulation system requires handcrafted hardware description language ( HDL ) and significant design efforts . This paper explores FPGA-based EMT real-time simulation system design using high-level synthesis ( HLS ) technology to accelerate the design process and improve the flexibility , because high-level language can be transferred into HDL using HLS . Furthermore , loop unroll and pipeline optimisation directives are analysed and applied for HLS to improve the computation speed of the components .
A new HW/SW co-design skin segmentation step to locate the eyes/face is proposed . Our presented new approach migrates the skin segmentation step from processing system ( SW ) to programmable logic ( HW ) IP taking the advantage of High Level Synthesis ( HLS ) tool flow to accelerate the design and prototyping flow of the vision based ADAS on a hardware Zynq platform .
Buffering frequently reused data on chip is a common approach to address this problem and the optimization of the cache architecture introduces yet another complex design space . This paper presents a high-level synthesis ( HLS ) design aid that generates parallel application-specific multi-scratchpad architectures including on-chip caches . Our program analysis identifies non-overlapping memory regions , supported by private scratchpads , and regions which are shared by parallel units after parallelization and which are supported by coherent scratchpads and synchronization primitives .
Modern high-level synthesis (HLS)-based tools allow for the creation of complex systems where parts of the user 's software are executed on a conventional processor , and the other parts are implemented as hardware accelerators via HLS flows . While modern tools allow designers to construct these systems relatively quickly , observing and debugging the real-time execution of these complex systems remains challenging .
I describe this new IR – the Value State Flow Graph ( VSFG ) – and how it statically exposes ILP from control-flow intensive code by enabling control-dependence analysis , execution along multiple flows of control , as well as aggressive control-flow speculation . I also present a High-Level Synthesis ( HLS ) toolchain , that compiles unmodified high-level language code to dataflow custom hardware , via the LLVM compiler infrastructure . I show that for control-flow intensive code , VSFG-based custom hardware performance approaches , or even exceeds the performance of a complex superscalar processor , while consuming only 1/4 × the energy of an efficient in-order processor , and 1/8 × that of a complex out-of-order processor .
LegUp [ 1 ] is an open-source high-level synthesis ( HLS ) tool that accepts a C program as input and automatically synthesizes it into a hybrid system . The hybrid system comprises an embedded processor and custom accelerators that realize user-designated compute-intensive parts of the program with improved throughput and energy efficiency .
To effectively implement this adder we use the concept of " Hybrid Variable Latency " technique . The Performance parameters of the adder are reported using Cadence tool ( RTL-Compiler ) in 45-nm technology and functionality of the design is validated using Vivado HLS . In comparison with the existing techniques , the proposed scheme is relatively faster and also it shows improvement in delay by 7.19 % and 15.63 % respectively .
From acommon source script , Sonar can generate testbenches writtenin SystemVerilog ( SV ) and C++ . These files can then beimported into standard simulation tools such as ModelSim[2 ] or Vivado HLS and run . The use of Python makes iteasy to extend Sonar with higher layers of abstraction fortestbenches and integrate it with other software platforms .
However , the cost and difficulty of hardware design is often prohibitive , and consequently , a software approach is used for most applications . In this article , we introduce a new high-level synthesis tool called LegUp that allows software techniques to be used for hardware design . LegUp accepts a standard C program as input and automatically compiles the program to a hybrid architecture containing an FPGA-based MIPS soft processor and custom hardware accelerators that communicate through a standard bus interface .
While the EDSL is not pure C , it provides a user-friendly language that can be trivially translated from a recursive C program , and can contain calls to plain C for compute-intensive leaf functions . HLSRecurse is a platform independent library that allows the same program to be compiled using g++ , clang , Legup , or Vivado HLS , and have the same execution semantics . The performance of HLSRecurse is evaluated in software and hardware using three micro-benchmarks of the underlying state-machine builder , and five practical examples of real recursive programs including a Sudoku solver , Strassen multiplication , and adaptive Monte-Carlo integration .
LegUp is an open-source high-level synthesis framework that simplifies the hardware accelerator design process [ 8 ] . With LegUp , a designer can start from an embedded application running on a processor and incrementally migrate portions of the program to hardware accelerators implemented on an FPGA . The final application then executes on an automatically-generated software/hardware coprocessor system . This paper presents on overview of the LegUp design methodology and system architecture , and discusses ongoing work on profiling , hardware/software partitioning , hardware accelerator quality improvements , Pthreads/OpenMP support , visualization tools , and debugging support .
In the context of parallel hardware , we investigate architectural and memory optimizations that help to improve circuit performance and area , and discuss a method of using the producer-consumer pattern in software to infer a streaming circuit in hardware . With these techniques , we show that LegUp can produce high-performance hardware that can be competitive to circuits that are generated by commercial HLS tools , and demonstrate that LegUp-generated circuits can also outperform software executing on x86 processors .
LegUp supports the synthesis of most of the C language to hardware , including loops , structs , multi-dimensional arrays , pointer arithmetic , and floating point operations . The LegUp distribution includes the CHStone HLS benchmark suite , as well as a test suite and associated infrastructure for measuring quality of results , and for verifying the functionality of LegUp-generated circuits . LegUp is freely downloadable at www.legup.org , providing a powerful platform that can be leveraged for new high-level synthesis research .
However , such standard cores can be over provisioned or lack specific specializations as required by applications . We consider FP cores described in the C language , synthesized to hardware using the LegUp high-level synthesis ( HLS ) tool [ 1 ] . Their software specification permits straightforward customization to non-compliant variants having superior area and performance characteristics , such as reduced-precision floating point , or cores without full IEEE 754 exceptions support .
NoCs provide scalable connectivity between FPGA datapaths and memory banks and allow parallel routing of packets from datapaths to the banks . We develop a LegUp 5.0 compiler pass that ( 1 ) handles loops with indirect memory access by inserting NoCs into the circuit as required , ( 2 ) provides a performance and resource tuning framework for optimizing the resulting hardware , and ( 3 ) obviates the need for NoC expertise during programming . We quantify the effectiveness of our approach across a range of kernels with indirect accesses by comparing against baseline LegUp 5.0 targeting a Xilinx VC707 board .
In addition , we show that we can support the pipelining of loops containing atomics by injecting further inter-iteration constraints . We implement our approach on two constraint-based scheduling HLS tools : LegUp 4.0 and LegUp 5.1 . We extend both tools to support two memory models that are capable of synthesising atomics correctly .
For this experimental purpose , I defined benchmarks into DLP / HLS catalogues , which are " data " and " function " intensive respectively . The programs of DLP will be executed in LEON3 MP and LE1 CMP multi-processor systems and the programs of HLS in the LegUp Co-design system on target FPGAs . In preliminary , the performance of the soft-core processors will be examined by executing all the benchmarks .
The hybrid system comprises an embedded processor and custom accelerators that realize user-designated compute-intensive parts of the program with improved throughput and energy efficiency . In this paper , we overview the LegUp framework and describe several recent developments : 1 ) support for an embedded ARM processor , as is available on Altera 's recently released SoC FPGA , 2 ) HLS support for software parallelization schemes -- pthreads and OpenMP , 3 ) enhancements to LegUp 's core HLS algorithms that raise the quality of the auto-generated hardware , and , 4 ) a preliminary debugging and verification framework providing C source-level debugging of HLS hardware . Since its first release in 2011 , LegUp has been downloaded over 1000 times by groups around the world , providing a powerful platform for new research in high-level synthesis algorithms and embedded systems design .
By using HLS in a framework for architectural exploration of digital hardware , the number and diversity of architectural variations that can be generated and evaluated is far greater than what could have been done manually . During a previous project , the HLS-tool LegUp was explored . The goal was to see if the tool could be used the described framework .
To gain insight on how to use FPGAs in high-performance computing , we created four different systems and evaluated them using benchmarks from the Rodinia benchmark suite . The systems we evaluated were diverse in both programming model and generality , and range from a custom-built 30-core manycore system to FSM-based accelerators using LegUP and deep data-flow pipelines using Intel FPGA SDK for OpenCL . We found that the original version of LegUp does not achieve very good performance out of the box ; still , with some non-trivial modification in the architecture , we improved its performance by up to 10 times .
In this paper , the design details of square root module are discussed from the viewpoint of resource and speed optimization . For a n-bit number , with an appropriate algorithm and high-level synthesis design method , it can be computed only by using 2n+ log2n -3 bits Registers and a Comparator in n/2 clocks .
We believe that programmability of future heterogeneous computing platforms has brought a new urgency to bear on several old problems in high-level synthesis for FPGAs . Our focus is on the two areas we believe are most underdeveloped in today ’s high-level synthesis software : eective utilisation of the numerical exibility aorded by high-level correctness specications , and application-specic memory subsystem synthesis . We conclude with our perspective on the likely future evolution of the eld .
We believe that programmability of future heterogeneous computing platforms has brought a new urgency to bear on several old problems in high-level synthesis for FPGAs . Our focus is on the two areas we believe are most underdeveloped in today ’s high-level synthesis software : effective utilisation of the numerical flexibility afforded by high-level correctness specifications , and application-specific memory subsystem synthesis . We conclude with our perspective on the likely future evolution of the field .
Along with the development of VLSI technology and the trend of system-on-chip design , traditional high-level synthesis can not deal with relatively complexity of system-on-chip design . In order to achieve optimal resource allocation , meet its performance and power requirements , and reduce its design time , we need a high-level synthesis software dealing system-level behavior . In consideration of system complexity , we have proposed a high-level synthesis method that synthesis for the task-level grains in a system behavior .
Another advantage of our technique is that CLB boundaries in the final design are aligned to RTL module boundaries providing modularity and ease in testing . Thus this technique is very suitable for integration as a technology mapping phase with a high-level synthesis package .
A digital module for the relay station is designed in order to relay data to and from the BISC chip . The digital module is written in SystemC and synthesized for FPGA fabric using a high- level synthesis tool . The module successfully interfaces to the PCB and is capable of relaying 83.2 Mbit/s of data from the BISC chip to memory .
We compare the results obtained with the execution of the same benchmarks written in OpenCL , in the same architectures . The results show that OMPSs greatly outperforms the OpenCL environment . It is more flexible to exploit multiple accelerators .
The resulting scheme exhibits interesting conservation and stability properties . For achieving high performance , the scheme is tested on recent multi-core processors and GPUs , using the OpenCL environment .
The resulting scheme exhibits interesting conservation and stability properties . For achieving high performance , the scheme is tested on recent muulticore processors and GPU , using the OpenCL environment .
This work introduces CLBlast , an open-source BLAS library providing optimized OpenCL routines to accelerate dense linear algebra for a wide variety of devices . It is targeted at machine learning and HPC applications and thus provides a fast matrix-multiplication routine ( GEMM ) to accelerate the core of many applications ( e.g. deep learning , iterative solvers , astrophysics , computational fluid dynamics , quantum chemistry ) .
Our experiments indicate that the MPI-CUDA codes based on FDM and FEM achieves nearly 6x speed-ups for large mesh sizes in comparison to host-cpu implementation of the same code . The un-optimized OpenCL implementation GPU times have shown marginal improvement in speed-ups whereas counterpart the CUDA codes achieved maximum speedup of 4x to 6x on HPC GPU Cluster . We presented performance analysis for different mesh sizes that prove performance capabilities of performance and scalability of FDM and FEM computations GPU cluster .
On the other hand , providing high performance in the presence of irregular memory access patterns to off-chip memory remains a challenge for the automated synthesis flows . In this paper , we study the performance/energy characteristics of OpenCL-generated FPGA designs on irregular memory access patterns , targeting XSBench , a memory-intensive Monte Carlo simulation code , as a case study . To complete our study , we implement XSBench in OpenCL and study optimization strategies for FPGAs .
While there are benchmarks for measuring the sustained memory bandwidth for CPUs and GPUs , such a benchmark for FPGAs has been missing . We present MP-STREAM , an OpenCL-based synthetic micro-benchmark for measuring sustained memory bandwidth , optimized for FPGAs , but which can be used on multiple platforms . Our main contribution is the introduction of various generic as well as device-specific parameters that affect memory bandwidth .
In particular , we will cover architectural features of FPGAs that make them well suited to many complex operations , including matrix multiplications and convolutions . In addition , we will introduce you to programming FPGAs using the Intel FPGA SDK for OpenCL ™ , and how specific OpenCL coding techniques can lead to efficient circuits implemented on the FPGA . Finally , we will go over several case studies where FPGAs have shown very competitive performance when programmed using OpenCL , including convolutional neural nets , FFTs , and astronomy de-dispersion algorithms .
In this work , FFTS has been modified to dynamically exploit streaming store instructions on x86 machines , resulting in speedups of over 10 % , Also , when dynamic code generation is prohibited on some mobile platforms , FFTS has been altered to avoid it , while maximizing the performance . In paper Computer Vision Accelerators for Mobile Systems based on OpenCL GPGPU Co-Processing , Wang , Xiong , W. J. Gross ( * ) Department of Electrical and Computer Engineering , McGill University , Montreal , Quebec , Canada e-mail : warren.gross@mcgill.ca
However , FPGAs are capable of far more than acceleration , hence it is interesting to explore if OpenCL can be used to take advantage of FPGAs for more generic applications . To answer these questions , especially in the context of High Energy Physics , two applications , a DAQ module and an acceleration workload , were tested for implementation with OpenCL on FPGAs2 . The challenges on using OpenCL for a DAQ application and their solutions , together with the performance of the OpenCL based acceleration are discussed .
Then , we provide the schedulability test through rigorous theoretical analysis . We conduct extensive simulations , and the results demonstrate that the proposed scheduling algorithm and analysis significantly outperform existing ones .
A system framework was set up combining advantages of distributed scheduling and centralized scheduling based on multi-agents.,The framework included a centralized part and a multi-agent scheduling net . The former adopted optimized Genetic Algorithm(GA ) to select diversified scheduling schemes , and the latter consisted of resource agent and cooperation protocol to keep well adaptation to dynamic manufacturing environment . Priority of each order was coded as a gene in a chromosome with each chromosome representing a scheduling scheme . Sequence processing time was arranged by multi-agent scheduling net according to order priority and scheduling protocol . Then the fitness value was calculated based on the Early/Tardiness value of each order obtained through multi-agent scheduling net . Based on this calculation , an optimized scheduling scheme was selected .
We propose a playback length changeable 3D video data chunk segmentation algorithm . A novel hybrid-priority based 3D video P2P data scheduling algorithm is presented . Bandwidth-adaptive 3D video P2P streaming for heterogeneous networks is implemented .
A terminal to provide a method for receiving a downlink data channel in a wireless communication system . A method according to the invention , step a , first schedule information for the terminal specific reference signal based downlink data channel for receiving information about one or more settings that define a large characteristic of the UE-specific reference signal and detecting any one of the second schedule information , wherein the receiving terminal specific reference signal based downlink data channel , the first schedule information , the one of the one or more settings includes an indicator for indicating a large characteristic defined is defined by the set indicated by indicator during the first schedule information detected at the second scheduling information detection , by setting already set out the one or more settings is the fact characterized . .The
These results motivate the development and assist the evaluation of two polynomial-time heuristic algorithms for 2-processor scheduling with limited memory . For constant degree graphs it is shown that the heuristics can be use¬ ful scheduling tools . It is also shown that use of the inclusive-cuts graph can lead to an improvement in perform¬ ance , but at the expense of additional scheduling overhead .
Fatalities require the execution of close out procedures , which take a specified period of time ( and must be given precedence over living patients ) . Through simulation we compare the current defacto scheduling processes in use within CCUs , against a new scheduling algorithm that makes use of an integrated array of patient information collected by a hypothetical vital sign integration infrastructure . Our simulation study provides quantitative evidence from which we can measure the extent to which such an infrastructure reduces risk to CCU patients and lowers operational personnel costs .
Spiral allows a stretch of O(log(n)2 log(D ) ) where n is the number of nodes in the network and D the diameter of the network . Finally the third parallel was between oblivious routing in networks and load balance transactional scheduling in NUMA . Jean-Philippe Martin presented his joint work with Christopher J. Rossbach on a model that predicts the performance for distributed software transactional memory ( DSTM ) executing a given workload .
This would bring the load-unbalancing problem when the topology run failed and is killed by its user , or more new machines are extended in Storm cluster . Aiming at solving them , we design the S-Storm , a slot-aware scheduling strategy for even scheduler in Storm , which achieves a fine-grained EvenScheduler using the slot-aware sorting queue and merger factor . S-Storm has the following desirable features : 1 ) It evenly allocates slots for multi-topologies in a load-balancing cluster .
In Commercial grids , users are regularly engaged with the distributed resource providers with whom they have little or no prior experience . The interaction between these distributed resource providers requires resource management and scheduling solutions . The allocation and scheduling of applications on a set of heterogeneous , dynamically changing resources is a serious problem .
Synthesis of statically analyzable accelerator networks from sequential programs Machine learning on FPGAs to face the IoT revolution FPGA Acceleration for Simultaneous Medical Image Reconstruction and Segmentation Unified System Construction (USC) Estimating BIST Resources in High-level Synthesis An algorithm for partitioning of application specific systems High-level synthesis of dataflow programs for heterogeneous platforms Improving high level synthesis optimization opportunity through polyhedral transformations Accelerating 2D FFT with Non-Power-of-Two Problem Size on FPGA Application of Shifted Popov Form in High-level Synthesis and Verification for Reconfigurable Architectures Minimum-Period Register Binding Multicomponent Synthesis for Multichip Modules (MCMs) Direct synthesis of timed asynchronous circuits Spector: An OpenCL FPGA benchmark suite Implementation of an adaptive filter robust to impulsive noise A dynamic reconfigurable processor and a design tool for the next generation ECUs ParaFPGA 2017: Enlarging the Scope of Parallel Programming with FPGAs Implementation of Sayeh Processor in VHDLUsing Functional and timing in-hardware verification of fpga-based designs using unit testing frameworks From C to elastic circuits Exploration of FPGA-based electromagnetic transient real-time simulation system design using high-level synthesis A hardware skin-segmentation IP for vision based smart ADAS through an FPGA prototyping MATCHUP: Memory Abstractions for Heap Manipulating Programs Unified On-Chip Software and Hardware Debug for HLS-Accelerated Programs Accelerating control-flow intensive code in spatial hardware Automating the Design of Processor/Accelerator Embedded Systems with LegUp High-Level Synthesis Design of Delay Efficient Hybrid Adder for High Speed Applications Sonar: Writing Testbenches through Python LegUp: An open-source high-level synthesis tool for FPGA-based processor/accelerator systems Synthesisable recursion for C++ HLS tools From software to accelerators with LegUp high-level synthesis From software threads to parallel hardware with LegUp high-level synthesis High-level synthesis with LegUp: a crash course for users and researchers High-level synthesis of software-customizable floating-point cores LegUp-NoC: High-Level Synthesis of Loops with Indirect Addressing Scheduling Weakly Consistent C Concurrency for Reconfigurable Hardware Performance and area evaluations of processor-based benchmarks on FPGA devices Automating the Design of Processor/Accelerator Embedded Systems with LegUp High-Level Synthesis High-Level Synthesis for Application-Specific Integrated Circuit Implementation using LegUp Evaluating high-level design strategies on FPGAs for high-performance computing High-level Synthesis Design of a Squqre Root Unit Whither Recongurable Computing Whither Reconfigurable Computing Software Design of A Task-level High Level Synthesis Method An Efficient Technique for Mapping RTL Structures onto FPGAs Embedded System Design for Bioelectronic Interface to Sensory Cortex Optimizing the Exploitation of Multicore Processors and GPUs with OpenMP and OpenCL OpenCL simulations of two-fluid compressible flows with a random choice method OpenCL Numerical Simulations of Two-Fluid Compressible Flows With a 2D Random Choice Method CLBlast: A Tuned OpenCL BLAS Library C-DAC's efforts: application kernels on HPC cluster with GPU accelerators Evaluating irregular memory access on OpenCL FPGA platforms: A case study with XSBench MP-STREAM: A Memory Performance Benchmark for Design Space Exploration on Heterogeneous HPC Devices Harnessing the Power of FPGAs with the Intel FPGA SDK for OpenCL™ Latest Advances on Design and Implementation of DSP Systems Evaluation of 'OpenCL for FPGA' for Data Acquisition and Acceleration in High Energy Physics Mixed Criticality Scheduling for Industrial Wireless Sensor Networks Integrated scheduling system framework based on genetic algorithm and agent Scalable 3D video streaming over P2P networks with playback length changeable chunk segmentation How downlink data channel reception in a multiple cell-based wireless communication system and apparatus therefor Heuristic algorithms for distributed processor scheduling with limited memory An evaluation of the risk impact of device heterogeneity on critical care delivery Making wireless algorithm theory more useful: five ideas from the 2013 workshop on realistic models for algorithms in wireless networks S-Storm: A Slot-Aware Scheduling Strategy for Even Scheduler in Storm Trust Based Grid Scheduling Algorithm for Commercial Grids
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ICCAD ICCAD FCCM FPGA ReConFig ICCAD FPT PARCO ACSSC ICUFN FPGA FPT EUC FCCM ASAP CASES FPGA DATE FCCM EUC FPL FPL LCPC IWOCL FPL IPDPS Workshops IWOCL HPCC/SmartCity/DSS
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