Auto-tuning Fixed-point Precision with TVM on RISC-V Packed SIMD Extension

• Published in: ACM transactions on design automation of electronic systems 2023-03, Vol.28 (3), p.1-21, Article 33
• Main Authors: Yang, Chun-Chieh, Chen, Yi-Ru, Liao, Hui-Hsin, Chang, Yuan-Ming, Lee, Jenq-Kuen
• Format: Article
• Language: English
• Subjects: Computer systems organization; Computing methodologies; Embedded software; Hardware; Machine learning approaches; Power estimation and optimization
• ISSN: 1084-4309; 1557-7309
• DOI: 10.1145/3569939

Today, as deep learning (DL) is applied more often in daily life, dedicated processors such as CPUs and GPUs have become very important for accelerating model executions. With the growth of technology, people are becoming accustomed to using edge devices, such as mobile phones, smart watches, and VR devices in their daily lives. A variety of technologies using DL are gradually being applied to these edge devices. However, there is a large number of computations in DL. It faces a challenging problem how to provide solutions in the edge devices. In this article, the proposed method enables a flow with the RISC-V Packed extension (P extension) in TVM. TVM, an open deep learning compiler for neural network models, is growing as a key infrastructure for DL computing. RISC-V is an open instruction set architecture (ISA) with customized and flexible features. The Packed-SIMD extension is a RISC-V extension that enables subword single-instruction multiple-data (SIMD) computations in RISC-V architectures to support fallback engines in AI computing. In the proposed flow, a fixed-point type that is supported by an integer of 16-bit type and saturation instructions is added to replace the original 32-bit float type. In addition, an auto-tuning method is proposed to use a uniform selector mechanism (USM) to find the binary point position for fixed-point type use. The tensorization feature of TVM can be used to optimize specific hardware such as subword SIMD instructions with RISC-V P extension. With our experiment on the Spike simulator, the proposed method with the USM can improve performance by approximately 2.54 to 6.15× in terms of instruction counts with little accuracy loss.