Abstract
Development of ultra-compact, low-to-medium precision analog-to-digital
converters (ADCs) with unprecedented energy-efficiency is essential to
meet the ever-increasing demand for data converters in advanced
computing systems including neuromorphic accelerators based on emerging
non-volatile memories. To this end, in this work, for the first time, we
propose a feedforward neural network ADC based on a network of highly
scalable, CMOS-compatible, and energy-efficient ferroelectric-FinFET
(Fe-FinFET) synaptic elements. Our lower triangular neural network
(LTNN) ADC design, implemented using 7-nm technology from ARM along with
an experimentally calibrated compact model for Fe-FinFETs, consumes 5.44
μW of power, 1.03 μm2 of area while operating at a
speed of 1.23 megasamples per second for 4-bit precision. The proposed
neural network ADC may pave the way for realization of highly efficient
neuromorphic processing engines and neuro-optimizers based on
cross-point array of emerging non-volatile memories.