Optimizing Multiplier Design with Deep Reinforcement Learning

A reinforcement learning-based framework, RL-MUL, is proposed to efficiently optimize the design of multipliers and fused multiply-accumulators (MACs) by leveraging matrix and tensor representations to enable seamless integration of deep neural networks as the agent network.