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On the most performance critical part of\n+this problem is how to get high performance kernel implementation for growing model\n+architectures and hardware platforms.\n+\n+To address this challenge, TVM takes a full stack compiler approach. Combining code generator and auto-tuner in TVM,\n+we are able to generate kernels that are comparable to heavily hand-optimized libraries,\n+and obtain the state-of-the-art inference performance on hardware platforms including\n+ARM CPUs, Intel CPUs, Mali GPUs, NVIIDA GPUs and AMD GPUs.\n+\n+In this blog post, I will show the workflow of automatic kernel optimization in TVM compiler stack and \n+benchmark results on several hardware platforms.\n+\n+# System Overview\n+\n+{:center: style=\"text-align: center\"}\n+![image](/images/autotune-all/circle.png){: width=\"35%\"}\n+{:center}\n+<center> Figure 1. System Overview </center> <p></p>\n+\n+Kernel optimization in TVM is done in an iterative loop fashion.\n+As shown in Figure 1, the automatic kernel optimization takes a neural network (typically in computational graph representation)\n+from frontend frameworks as input, and generates kernels for all the operators in this network.\n+\n+The inner loop consists of scalable RPC runtime, machine learning based tuners and a tensor 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