MuRISCV-NN: Challenging Zve32x Autovectorization with TinyML Inference Library for RISC-V Vector Extension

Philipp Van Kempen, Jefferson Parker Jones, Daniel Mueller-Gritschneder, Ulf Schlichtmann

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

With the rapid adoption of deep learning workloads to resource-constrained edge devices, efficient and data-parallel computing paradigms are becoming increasingly important. The RISC-V ISA provides a set of vector extensions featuring powerful data computation capabilities to accelerate deep learning workloads at the edge. However, the RISC-V ecosystem lacks a lightweight, open-source, and vendor-agnostic compute library to support these extensions on embedded platforms. After porting the existing ARM Cortex-M specific kernel implementation to the RISC-V vector ISA, we optimized the operator implementations to make the most out of the data-level parallelism provided by supported targets. In comparison to programs vectorized by LLVM's built-in auto-vectorizer, we see an up to 60% advantage in runtime for convolutional models and large vectors while introducing less ROM overheads. Furthermore, muRISCV-NN integrates well with existing ML deployment frameworks, is bit-accurate to CMSIS-NN, and can, thus, be used as a drop-in replacement with minimal changes to the compilation flow.

Original languageEnglish
Title of host publicationProceedings of the 21st ACM International Conference on Computing Frontiers 2024 Workshops and Special Sessions, CF 2024 Companion
PublisherAssociation for Computing Machinery, Inc
Pages75-78
Number of pages4
ISBN (Electronic)9798400704925
DOIs
StatePublished - 7 May 2024
Event21st ACM International Conference on Computing Frontiers, CF 2024 - Ischia, Italy
Duration: 7 May 20249 May 2024

Publication series

NameProceedings of the 21st ACM International Conference on Computing Frontiers 2024 Workshops and Special Sessions, CF 2024 Companion

Conference

Conference21st ACM International Conference on Computing Frontiers, CF 2024
Country/TerritoryItaly
CityIschia
Period7/05/249/05/24

Keywords

  • Compilers
  • Neural Network Inference
  • RISC-V
  • Vectorization

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