<div>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 μm<sup>2</sup> 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.</div>
<div>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 μm<sup>2</sup> 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.</div>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.