ANN-based Performance Estimation of Embedded Software for RISC-V Processors

Abstract

The demand for optimized and efficient embedded software is increasing in many applications such as the Internet of Things (IoT) or other Cyber-Physical Systems (CPS). Hence, early performance analysis of embedded software is essential to perform Design Space Exploration (DSE), ensure efficiency, and meet time-to-market constraints. Designers usually use real hardware, simulators, or static analyzers to obtain the performance. However, these methods suffer from serious drawbacks as real hardware is not available in the early stage of the design process, simulators either do not support any timing accuracy or require large execution time, and static analyzers need details of the hardware microarchitecture. In this paper, we present a novel Artificial Neural Network (ANN)-based approach that allows a fast and accurate performance estimation of embedded software for RISC-V processors in the early design phases. This can significantly reduce the burden on designers to perform DSE. The proposed approach takes advantage of the dynamic analysis technique and analytical models and does not require any microarchitecture-related parameters such as cache misses, cache hits, and memory-level parallelism. We compare our proposed microarchitecture-independent approach with state-of-the-art in terms of speed and accuracy. Our experiments on various benchmarks demonstrate that the proposed approach achieves a speed-up of $4.41\times$ compared to a RISC-V Virtual Prototype (VP) at the Electronic System Level (ESL), while the estimation results have only a Mean Absolute Percentage Error (MAPE) of 2%.