giuseros opened a new pull request #6445:
URL: https://github.com/apache/incubator-tvm/pull/6445
### High level description of the submission
We added two new intrinsics in: `topi/arm_cpu/tensor_intrin.py`, namely
- `mmla4x4`: compute a matrix multiplication between tile `A(4,4)` and tile
`B(4,4)`
- `mmla16x4`: compute a matrix multiplication between tile `A(rows,4)` and
tile
`B(4,16)`
Then we used those intrinsics in two separate strategies. We added the
strategies in `topi/arm_cpu/conv2d_int8.py` and implemented the schedules
in `topi/arm_cpu/conv2d_gemm.py`. In particular:
- `schedule_conv2d_gemm`, when accelerated, packs matrix `A`, compute GEMM,
and unpack the resulting matrix. This uses the `mmla4x4` intrinsic
- `schedule_conv2d_gemm_hybrid` doesn't do any packing on `A` and `C` which
are in native form. This uses the `mmla16x4` intrinsic
Please note that for the limitations of `tensorize` we need to pad
matrix `A` in both cases (when dimensions are not multiple of the tiling
shape)
### RFC
This PR is based on the following RFC:
https://discuss.tvm.apache.org/t/rfc-accelerate-quantized-convolution-through-dot-product/7873
Change-Id: Id0d818d84ffc458c6dad7983fd350a0f3d5db395
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