An interesting work, with some to-be-addressed questions:
1.The paper only covers the GEMM part with small-scale experiments(CIFAR-10/100), not covering convolution, not covering GEMM part in more popular network such as Transformer/BERT, etc.
2. It is still an approximating method, meaning potential accuracy loss. So I think this method is less attractive to training acceleration scenario, maybe potentially as a complementing methods for inference acceleration.
3. No results evaluated in GPU with TensorCore equipment. I am a little bit curious, since modern AI accelerator(including NV GPU) all incorporate TensorCore which by-design supports GEMM acceleration, what is the add-on value brought by the approximating method mentioned in this paper.