Latency-Constrained Neural Architecture Search Method for Efficient Model Deployment on RISC-V Devices

Abstract

The rapid development of the RISC-V instruction set architecture (ISA) has garnered significant attention in the realm of deep neural network applications. While hardware-aware neural architecture search (NAS) methods for ARM, X86, and GPUs have been extensively explored, research specifically targeting RISC-V remains limited. In light of this, we propose a latency-constrained NAS (LC-NAS) method specifically designed for RISC-V. This method enables efficient network searches without the requirement of network training. Concretely, in the training-free NAS framework, we introduce an RISC-V latency evaluation module that includes two implementations: a lookup table and a latency predictor based on a deep neural network. To obtain real latency data, we have designed a specialized data collection pipeline for RISC-V devices, which allows for precise end-to-end hardware latency measurements. We validate the effectiveness of our method in the NAS-Bench-201 search space. Experimental results demonstrate that our method can efficiently search for latency-constrained networks for RISC-V devices within seconds while maintaining high accuracy. Additionally, our method can easily integrate with existing training-free NAS approaches.