  Recent works propose neural network- (NN-) inspired analog-to-digital
converters (NNADCs) and demonstrate their great potentials in many emerging
applications. These NNADCs often rely on resistive random-access memory (RRAM)
devices to realize the NN operations and require high-precision RRAM cells
(6~12-bit) to achieve a moderate quantization resolution (4~8-bit). Such
optimistic assumption of RRAM resolution, however, is not supported by
fabrication data of RRAM arrays in large-scale production process. In this
paper, we propose an NN-inspired super-resolution ADC based on low-precision
RRAM devices by taking the advantage of a co-design methodology that combines a
pipelined hardware architecture with a custom NN training framework. Results
obtained from SPICE simulations demonstrate that our method leads to robust
design of a 14-bit super-resolution ADC using 3-bit RRAM devices with improved
power and speed performance and competitive figure-of-merits (FoMs). In
addition to the linear uniform quantization, the proposed ADC can also support
configurable high-resolution nonlinear quantization with high conversion speed
and low conversion energy, enabling future intelligent analog-to-information
interfaces for near-sensor analytics and processing.
