ORIGINAL RESEARCH article
Front. Electron.
Sec. Integrated Circuits and VLSI
Adiabatic Capacitive Neuron: An Energy-Efficient Functional Unit for Artificial Neural Networks
Provisionally accepted
- University of Edinburgh, Edinburgh, United Kingdom
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This paper introduces a new, highly energy-efficient, Adiabatic Capacitive Neuron (ACN) hardware implementation of an Artificial Neuron (AN) with improved functionality, accuracy, robustness and scalability over previous work. The paper describes the implementation of a single neuron with 12 1-bit input synapse, supporting positive and negative weights, in an 0.18µm CMOS technology. The paper also presents a new Threshold Logic (TL) design for a binary AN activation function that generates a low symmetrical offset across three process corners and five temperatures between −55oC and 125oC. Post-layout simulations demonstrate a maximum rising and falling offset voltage of 9mV compared to conventional TL, which has rising and falling offset voltages of 27mV and 5mV respectively, across temperature and process. The total synapse energy saving for the proposed ACN was above 90% (over 12x improvement) when compared to an equivalent non-adiabatic CMOS Capacitive Neuron (CCN) for a frequency ranging from 500kHz to 100MHz. A 1000-sample Monte Carlo simulation including process variation and mismatch confirms energy savings of 90% compared to CCN in the synapse energy profile. Finally, the impact of supply voltage scaling shows consistent energy savings of above 90% (except all zero inputs) without loss of functionality.
Keywords: adiabatic, artificial neural networks, capacitive, energy recovery logic, Energy-efficient, Neuron, threshold logic
Received: 10 Nov 2025; Accepted: 09 Feb 2026.
Copyright: © 2026 Maheshwari, Smart, Raghav, Prodromakis and Serb. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Sachin Maheshwari
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