Multiplication by Inference using Classification Trees: A Case-Study Analysis

Inspired by cognitive functions of the human brain, machine learning-driven synthesis flows can map Boolean functions as Classification Trees that work like statistical inference engines. Circuits of this kind infer output values by evaluating the key features of the function learned during the training stage. We propose this idea for arithmetic circuits and, more specifically, for the design of an inferential 8-by-8 bit unsigned multiplier. Using as case-study an error-resilient image blending application, we quantify the most representative figures of merit, also giving comparison against a classical radix-4 multi-level implementation. Experimental results demonstrate the inferential multiplier guarantees 76% average accuracy, 22% less area, and 2× latency reduction that can be used for power optimization.