NeuADC: Neural Network-Inspired RRAM-Based Synthesizable Analog-to-Digital Conversion with Reconfigurable Quantization Support
Weidong Cao, Xin He, Ayan Chakrabarti and Xuan Zhang
Washington University in St.Louis
ABSTRACT
Traditional analog-to-digital converters (ADCs) employ dedicated analog and mixed-signal (AMS) circuits and require time-consuming manual design process. They also exhibit limited reconfigurability and are unable to support diverse quantization schemes using 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 desired quantization function using a neural network (NN) with a single hidden layer. Our design leverages the mixed-signal resistive random-access memory (RRAM) crossbar architecture in a novel dual-path configuration to realize basic NN operations at the circuit level and exploits smooth bit-encoding scheme to improve the training accuracy. Results obtained from SPICE simulations based on 130nm technology suggest that not only can NeuADC deliver promising performance compared to the state-of-art ADC designs across comprehensive design metrics, but also it can intrinsically support multiple reconfigurable quantization schemes using the same hardware substrate, paving the ways for future adaptable application-driven signal conversion. The robustness of NeuADC’s quantization quality under moderate RRAM resistance precision is also evaluated using SPICE simulations.
