ashutosh-arm commented on a change in pull request #15: URL: https://github.com/apache/tvm-rfcs/pull/15#discussion_r686026253
########## File path: rfcs/0013_Arm_CMSIS-NN_Integration.md ########## @@ -0,0 +1,118 @@ +- Feature Name: [RFC] Use CMSIS-NN with TVM +- Start Date: July 2021 +- RFC PR: https://github.com/apache/tvm-rfcs/pull/15 +- GitHub Issue: https://github.com/apache/tvm/issues/8646 + +# Acronyms +CMSIS: Common Microcontroller Software Interface Standard +ACL: The Compute Library for the Arm® Architecture +MLF: Model Library Format + +# Summary + +This RFC introduces plan of integration of CMSIS-NN library into TVM. It consists of efficient kernels targeted for Arm's Cortex-M architecture. + +Please refer to the following pages for more details on CMSIS-NN. +https://arm-software.github.io/CMSIS_5/NN/html/index.html +https://github.com/ARM-software/CMSIS_5/tree/develop/CMSIS/NN + +First PR in the series of PRs to fulfill this integration would be graph partitioner for softmax int8. Detailed plan can found below in this RFC. + + +# Motivation + +CMSIS-NN library consists of hand-tuned kernels that are suitable for Cortex-M and are compliant with the quantization scheme used in Tensorflow Lite. They have been optimized for better performance and small memory footprint which is required on these embedded devices and it would make sense for TVM to reuse these while generating code for Cortex-M. They have been integrated with the TensorFlow Lite Micro project. + + +# Guide-level explanation + +TVM's external code generation infrastructure allows for the automatic partitioning and code generation using the external compiler. Partitioned subgraphs containing operator(s) targeted for Cortex-M can then be translated into the CMSIS-NN C APIs which eventually become part of MLF. For this integration, we are heavily dependent on the TVM's infrastructure for external code generation. + +If a user runs tvmc, they will get a MLF format archive which calls out to the CMSIS operators. + +``` +tvmc --target=cmsisnn,c --output-format=mlf --executor=aot +``` + + +# Reference-level explanation + +We will enable this integration by considering TFLite networks, but is equally applicable for all other networks that can be translated into Relay IR. TFLite test that contains just a quantized (int8) softmax is first converted as a sequence of following relay operations: *dequantize -> softmax -> quantize* by the TFLite frontend. Please refer to the code snippet below. Review comment: Its relay modules obtained from the frontend. I have updated the explanation. Thanks for pointing it out. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
