Efficient Approximate Logic Synthesis with Dual-Phase Iterative Framework

Ruicheng Dai, Xuan Wang, Wenhui Liang, Xiaolong Shen, Menghui Xu, Leibin Ni, Gezi Li, Weikang Qian

Abstract: Approximate computing is an emerging paradigm to improve the energy efficiency for error-tolerant applications. Many iterative approximate logic synthesis (ALS) methods were proposed to automatically design approximate circuits. However, as the sizes of circuits grow, the runtime of ALS grows rapidly. Thus, a crucial challenge is to ensure circuit quality while improving the efficiency of ALS. This work proposes a dual-phase iterative framework to accelerate the iterative ALS flows. In the first phase, a comprehensive circuit analysis is performed to gather the necessary information, including the error information. In the second phase, minimal incremental computation is employed based on the information from the first phase. The experimental results show that the proposed method achieves an acceleration by up to 21.8 × without loss of circuit quality compared to the state-of-the-art methods.

External IDs:dblp:conf/date/DaiWLSXNLQ25