[apache/tvm] [RFC][Tracking Issue] CSI-NN2 Integration (Issue #11506)

### This issue to track upstreaming progress for CSI-NN2 integration. - [ ] P1. The CI environment support. - [ ] P2. The JSON generator with CSI-NN2 support and unit test for Conv2D. - [ ] p3. Unit tests for Softmax, Pooling, Relu, and Dense. -- Reply to this email directly or view it on GitHub

[apache/tvm-rfcs] [RFC][Backend] RFC-CSI-NN2-Integration (PR #75)

Introduce CSI-NN2 Compute Library into TVM to accelerate the inference performance of RISC-V CPU with Vector Extension. You can view, comment on, or merge this pull request online at: https://github.com/apache/tvm-rfcs/pull/75 -- Commit Summary -- * RFC-CSI-NN2-Integration -- File Changes

Re: [apache/tvm-rfcs] [RFC][Backend] RFC-CSI-NN2-Integration (PR #75)

> This is fantastic, thank you! We're excited to hear about the results for > MLPerf. My main comment is mostly concerned about documentation. I'm glad the > build instructions are included with the RFC, but I'd like to see the > inclusion of documentation about how to configure, build, and use

Re: [apache/tvm-rfcs] [RFC][Backend] RFC-CSI-NN2-Integration (PR #75)

> ok, then for CI do you plan to e.g. expand our `ci_qemu` Docker image to > additionally contain this custom qemu? (this involves committing a change to > `docker/`, then pinging a committer to update the version of the image used) Thanks for advice, we will add it. but I have no experience abo

Re: [apache/tvm-rfcs] [RFC][Backend] RFC-CSI-NN2-Integration (PR #75)

@areusch Thanks a lot. let me update the image. -- Reply to this email directly or view it on GitHub: https://github.com/apache/tvm-rfcs/pull/75#issuecomment-1153408149 You are receiving this because you are subscribed to this thread. Message ID:

Re: [apache/tvm-rfcs] [RFC][Backend] RFC-CSI-NN2-Integration (PR #75)

@areusch [a tracking issue](https://github.com/apache/tvm/issues/11506) is ready. I will keep updating it. -- Reply to this email directly or view it on GitHub: https://github.com/apache/tvm-rfcs/pull/75#issuecomment-1157154054 You are receiving this because you are subscribed to this thread.

Re: [apache/tvm-rfcs] [RFC] Adding initial SVE implementation (#18)

Thanks for bringing up. this is also very useful on RISC-V. we look forward to progress in this regard. -- Reply to this email directly or view it on GitHub: https://github.com/apache/tvm-rfcs/pull/18#issuecomment-1171880247 You are receiving this because you are subscribed to this thread. Me

[Apache TVM Discuss] [Development/RFC] Add some new tensorflow ops

## Motivation In order to enrich the support of TF operator and the need of practical work, we add 15 operators to TVM. as follows: * Expm1, Rint, Softsign, Cumprod, Cumsum * SegmentMax, SegmentMin, SegmentMean, SegmentPord, SegmentSum * UnsortedSegmentMax, UnsortedSegmen

[Apache TVM Discuss] [Development/RFC] Add some new tensorflow ops

I'll make them down into several prs, than send them directly. Thank you. --- [Visit Topic](https://discuss.tvm.apache.org/t/add-some-new-tensorflow-ops/8217/5) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https

[Apache TVM Discuss] [Development/RFC] Add some new tensorflow ops

We have tried using te.scan and te.compute to implement cumsum. But it seems that there is no way to use the same formula to adapt to all situations. Finally, we implement it with te.extern. --- [Visit Topic](https://discuss.tvm.apache.org/t/add-some-new-tensorflow-ops/8217/6) to respond

[Apache TVM Discuss] [Development] [Quantization] How to expose 'ndom_scale' 'nclip_min' & 'nclip_max' to TOPI or CodeGen

Hi, can this part of work be used in the main branch now? --- [Visit Topic](https://discuss.tvm.apache.org/t/quantization-how-to-expose-ndom-scale-nclip-min-nclip-max-to-topi-or-codegen/5393/8) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from