Digital approximate multipliers for machine learning and artificial intelligence applications

Abstract

Digital approximate multipliers (aMULT) utilizing interpolative apparatuses, circuits, and methods are described in this disclosure. The disclosed aMULT interpolative methods can be arranged or programmed to operate asynchronously and or synchronously. For applications where less precision is acceptable, fewer interpolations can yield less precise multiplication results, while such approximate multiplication can be computed faster and at lower power consumption. Conversely, for applications where higher precision is required, more interpolations can generate more precise multiplication results. As such, by utilizing the disclosed aMULT method, the resolution and precision objectives of an approximate multiplication function can be pre-programmed or adjusted real-time and or on the fly, which enables optimizing for different and flexible power consumption and speed of multiplication, in addition to enabling the optimization of an approximate multiplier's die size and cost in accordance with cost-performance objectives.