A Bit-level Sparsity-aware SAR ADC with Direct Hybrid Encoding for Signed Expressions for AIoT Applications

Ruicong Chen, H. T. Kung, Anantha P. Chandrakasan, Hae-Seung Lee

Abstract: In this work, we propose the first bit-level sparsity-aware SAR ADC with direct hybrid encoding for signed expressions (HESE) for AIoT applications. ADCs are typically a bottleneck in reducing the energy consumption of analog neural networks (ANNs). For a pre-trained Convolutional Neural Network (CNN) inference, a HESE SAR for an ANN can reduce the number of non-zero signed digit terms to be output, and thus enables a reduction in energy along with the term quantization (TQ). The proposed SAR ADC directly produces the HESE signed-digit representation (SDR) using two thresholds per cycle for 2-bit look-ahead (LA). A prototype in 65nm shows that the HESE SAR provides sparsity encoding with a Walden FoM of 15.2fJ/conv.-step at 45MS/s. The core area is 0.072mm2.