A Reconfigurable Approximate Floating-Point Multiplier with kNN

Abstract

Due to the high demands for computing, the available resources always lack. The approximate computing technique is the key to lowering hardware complexity and improving energy efficiency and performance. However, it is a challenge to properly design approximate multipliers since input data are unseen to users. This challenge can be overcome by Machine Learning (ML) classifiers. ML classifiers can predict the detailed feature of upcoming input data. Previous approximate multipliers are designed using simple adders based on ML classifiers but by using a simple adder-based approximate multiplier, the level of approximation cannot change at runtime. To overcome this drawback, using an accumulator and reconfigurable adders instead of simple adders are proposed in this paper. Also, the rounding technique is applied to approximate floating-point multipliers for further improvement. Our experimental results show that when the error tolerance of our target application is less than 5%, the proposed approximate multiplier can save area by 70.98%, and when the error tolerance is less than 3%, a rounding enhanced simple adders-based approximate multiplier can save area by 65.9% and a reconfigurable adder-based approximate multiplier with rounding can reduce the average delay and energy by 54.95% and 46.67% respectively compared to an exact multiplier.