A digitally controlled switched‐ring oscillator‐based time domain multiply‐and‐accumulate core for machine learning

Abstract

AbstractThis article presents a time domain multiply‐and‐accumulate (MAC) engine used for convolutional neural networks. Time domain is chosen for efficiency as it allows for compact representation of multi‐bit inputs on a single wire. This reduces gate count and switching capacitance (Cdyn) compared to traditional all‐digital implementation. The inputs are encoded by selecting a pulse of varying width depending on input code. The multiplication operation and accumulation is implemented using a digitally controlled switched‐ring oscillator time‐to‐digital converter functioning as a time accumulator. The digital control allows for accumulation and quantization of two signals simultaneously, halving the required time to quantize a certain value. The proposed MAC is designed in a 28 nm CMOS process and can achieve a simulated power efficiency of 0.32 pJ/b, which is 1.8 better than what can be achieved by a single input gated ring oscillator (GRO) design.