RL-MUL: Multiplier Design Optimization with Deep Reinforcement Learning

Autor:	Zuo, Dongsheng, Zhu, Jiadong, Ouyang, Yikang, Ma, Yuzhe
Rok vydání:	2024
Předmět:	Computer Science - Hardware Architecture Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Multiplication is a fundamental operation in many applications, and multipliers are widely adopted in various circuits. However, optimizing multipliers is challenging and non-trivial due to the huge design space. In this paper, we propose RL-MUL, a multiplier design optimization framework based on reinforcement learning. Specifically, we utilize matrix and tensor representations for the compressor tree of a multiplier, based on which the convolutional neural networks can be seamlessly incorporated as the agent network. The agent can learn to optimize the multiplier structure based on a Pareto-driven reward which is customized to accommodate the trade-off between area and delay. Additionally, the capability of RL-MUL is extended to optimize the fused multiply-accumulator (MAC) designs. Experiments are conducted on different bit widths of multipliers. The results demonstrate that the multipliers produced by RL-MUL can dominate all baseline designs in terms of area and delay. The performance gain of RL-MUL is further validated by comparing the area and delay of processing element arrays using multipliers from RL-MUL and baseline approaches. Comment: Extension of DAC 2023 version
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.00639 Zobrazit plný text záznamu View this record from Arxiv