Efficient Approximate Decomposition Solver using Ising Model

Weihua Xiao, Tingting Zhang, Xingyue Qian, Jie Han, Weikang Qian

Abstract: Computing with memory is an energy-efficient computing approach. It pre-computes a function and stores its values in a lookup table (LUT), which can be retrieved at runtime. Approximate Boolean decomposition reduces the LUT size for implementing complex functions, but it takes a long time to find a decomposition with a minimal error. In this work, to address this issue, we propose an efficient Ising model-based approximate Boolean decomposition solver. First, a new column-based approximate disjoint decomposition method is proposed to fit the Ising model. Then, it is adapted to the Ising model-based optimization solver. Moreover, two improvement techniques are developed for an efficient search of the approximate disjoint decomposition when using simulated bifurcation to solve the Ising model. Experimental results show that compared to the state-of-the-art work, our approach achieves a 11% smaller mean error distance with an average 1.16× speedup when approximately decomposing 16-input Boolean functions.