NeuADC: Neural Network-Inspired Synthesizable Analog-to-Digital Conversion

Abstract

Traditional analog-to-digital converters (ADCs) employ dedicated analog and mixed-signal (AMS) circuits, requiring time-consuming manual design process. They also exhibit limited configurability to support diverse quantization schemes on the same circuitry. In this paper, we propose NeuADC-an automated design approach to synthesizing an analog-to-digital (A/D) interface that can approximate the desirable quantization function using a neural network (NN) with a single hidden layer. We leverage the mixed-signal resistive random-access memory (RRAM) crossbar architecture to design a novel dual-path configuration for the implementation of the basic NN operations at the circuit level. We exploit alternative bits encoding scheme to the conventional binary encoding to improve the training accuracy. Our method incorporates nonidealities at the device and circuit level into the training process to ensure NeuADC's robustness against variations of process, supply voltage, and temperature (PVT). Results obtained from SPICE simulation based on RRAM and standard 130-nm CMOS technology suggest that not only can NeuADC deliver promising performance compared to the state-of-the-art ADCs and other emerging converter designs across comprehensive design metrics, but it can also intrinsically support multiple configurable quantization schemes using the same hardware substrate, paving ways for future adaptable application-driven signal conversion. Our systematic evaluations on the proposed NeuADC framework also quantify the impacts on the ADC quantization quality from hidden neuron sizes, RRAM resistance imprecision, and PVT variations, and reveal the design tradeoff between speed, power, and area in a NeuADC circuit.