RL-MUL: Multiplier Design Optimization with Deep Reinforcement Learning
<|reference_start|>RL-MUL: Multiplier Design Optimization with Deep Reinforcement Learning: 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.<|reference_end|>
@article{zuo2024rl-mul:,
    title={RL-MUL: Multiplier Design Optimization with Deep Reinforcement Learning},
    author={Dongsheng Zuo, Jiadong Zhu, Yikang Ouyang, Yuzhe Ma},
    journal={arXiv preprint arXiv:2404.00639},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00639},
    primaryClass={cs.AR cs.LG}
}