Presentation
Efficient Approximate Decomposition Solver using Ising Model
DescriptionComputing with memory is an energy-efficient computing approach. It pre-computes a function and store its values in a lookup table (LUT), which can be retrieved at runtime. Approximate Boolean decomposition has been recently proposed to reduce the LUT size for implementing complex functions, but it takes a long time to find a decomposition with a minimized error. As a parallel algorithm developed based on the Ising model, simulated bifurcation (SB) is promised to be a high-performance approach for combinatorial optimization. In this paper, we propose an efficient SB-based approximate function decomposition approach. Specifically, a new approximate disjoint decomposition method, called column-based approximate disjoint decomposition, is first 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 SB. The experiment results shows 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 16-output Boolean functions.
Event Type
Research Manuscript
TimeTuesday, June 254:30pm - 4:45pm PDT
Location3010, 3rd Floor
Design
Emerging Models of Computation