Recent advances in building Sparse Networks are with very promising results in terms of optimization and performance. With adequate settings, Sparse Network we can achieve the same accuracy(refer to base-lined FP32 networks) with fewer parameters and FLOPs. Though there are various techniques proposed and involved at different stages like Sparse codes, Sparse Kernels, Sparsifications, the area is still evolving at steep rate.
We are currently researching and working in the Sparse domain. As current TVM has support for few basic operations already, we like to work towards enhancing and strengthening the Sparse support, be it in terms of Sparse codes or Sparse Kernels. Also as different framework like Tensorflow, Pytorch, TFLite etc may realize Sparsity in their own ways. We will also work towards making the Sparse feature more robust and adaptable to various front-ends easily. Soon we will try to release a Tracking list with more internal details. --- [Visit Topic](https://discuss.tvm.apache.org/t/discuss-tvm-v0-8-roadmap/8139/5) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.apache.org/email/unsubscribe/7d694f9807981c5038975e634d622173e9802c012551a4b80e14841e59a61abf).
