FALSAx: An Integrated Framework for Accuracy and Logic Synthesis Estimation of Approximate Adders

This work proposes an integrated framework for accuracy and logic synthesis (LS) estimation of approximate adders (FALSAx). It represents a versatile and robust framework designed to estimate the accuracy, power, and area of various approximate adders (AxAs) for any input width ( W ) and K bits of approximation using machine learning (ML) models. FALSAx facilitates performance predictions and optimization for different AxAs configurations through meticulously curated datasets and ML-driven analysis. The framework's capability to automatically generate Pareto fronts from estimated values aids in identifying optimal trade-offs among crucial metrics, providing essential insights for circuit design and optimization. The FALSAx includes four internal frameworks: FrAQ, PILSE, and FELSE, which estimates dynamic power, total leakage power, and area, with frequency variations automatically, and the FALED dataset of the FALSAx. As a case study, this work analyzed 16 types of AxAs on FALSAx: AMA-V, AxPPA, COPY, TRUNC, ETA, LOA, HOERAA, LDCA, LZTA, HEAA, M-HEAA, HERLOA, M-HERLOA, HOAANED, OLOCA, and SETA. The rigorous analysis provided by FALSAx revealed that HERLOA, M-HERLOA, M-HEAA, and AxPPA demonstrated superior accuracy metrics such as SSIM, NCC, MAE, and MRE. Furthermore, power analysis showed that AxPPA exhibited the best power efficiency for lower approximation bits ( K <= 3 ). At the same time, gate-free adders like COPY, TRUNC, AMA-V, LDCA, and LZTA were more power-efficient for higher approximation bits ( K>3 ). Area estimations indicated that AxPPA maintained competitive efficiency for lower approximation bits ( K <= 5 ), while TRUNC and LDCA were more efficient for higher bits ( K>5 ).