Functional and Timing Models for a Programmable Hardware Accelerator
Classes of problems that exhibit high levels of data reuse have heavy overhead costs due to the necessity of repeated memory accesses. A proposed programmable hardware accelerator using a spatial architecture paired with triggered instructions could solve this class of problems more efficiently. Performance models are useful for evaluating the performance of different applications and design alternatives. This project develops a functional model for this hardware accelerator as well as a simple timing model to allow for examination of the performance of this proposed architecture. Furthermore, convolution, pooling, and non-linearity are implemented as programs on this accelerator in order to show functionality and flexibility present within the system. This project shows the feasibility of a spatial architecture powered by triggered instructions and provides the framework for future testing and prototyping for this problem space. Thesis Supervisor: Joel Emer Title: Professor of the Practice Thesis Supervisor: Vivienne Sze Title: Assistant Professor