[GitHub] [incubator-tvm] liangfu commented on issue #4392: [VTA] Enable streamlined GEMM execution

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liangfu commented on issue #4392: [VTA] Enable streamlined GEMM execution
URL: https://github.com/apache/incubator-tvm/pull/4392#issuecomment-557371295
 
 
   This PR doesn't intend to reduce cycle count, or any performance improvement.
   
   My major intention is to bring successful evaluation of `matrix_multiply_opt.py` and `convolution_opt.py` scripts, so that we can get closer to bring end-to-end support in evaluating resnet18. The reason the evaluation of these scripts failed previously is that 
   
   > This PR fixed an issue in the streamlined GEMM execution by disabling pipelined adder, which consumes 4 cycles (in case of LOG_BLOCK=4) in addition to the single-cycle fused multiplier-adder. This is much longer than the 4-stage streamline design in the TensorGemm module.
   
   Here are the benchmarks from Intel's Timing Analyzer (some entries still need to be updated)
   
   | design | area (in ALMs) | fmax (1100mV,  slow 100C) | fmax (1100mV,  slow -40C) | fmax (1100mV,  fast 100C) | fmax (1100mV,  fast -40C) | cycle count |
   | -- | -- | -- | -- | -- | -- | -- |
   | PipeAdderX4 | -- | -- | -- | -- | -- | -- |
   | PipeAdderX2 AdderX2 | -- | -- | -- | -- | -- | -- |
   | AdderX4 | 18,186/41,910 (43%) | 60.72 MHz | 58.82 MHz | 92.43 MHz | 114.97 MHz | -- |
   
   

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