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arXiv · 2512.14642

An Energy-Efficient Adiabatic Capacitive Neural Network Chip

Himadri Singh Raghav·Sachin Maheshwari·Mike Smart·Patrick Foster·Alex Serb
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Abstract

Recent advances in artificial intelligence, coupled with increasing data bandwidth requirements, in applications such as video processing and high-resolution sensing, have created a growing demand for high computational performance under stringent energy constraints, especially for battery-powered and edge devices. To address this, we present a mixed-signal adiabatic capacitive neural network chip, designed in a 130$nm$ CMOS technology, to demonstrate significant energy savings coupled with high image classification accuracy. Our dual-layer hardware chip, incorporating 16 single-cycle multiply-accumulate engines, can reliably distinguish between 4 classes of 8x8 1-bit images, with classification results over 95\%, within 2.7\% of an equivalent software version. Energy measurements reveal average energy savings between 2.1x and 6.8x, compared to an equivalent CMOS capacitive implementation.

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Himadri Singh Raghav, Sachin Maheshwari, Mike Smart, Patrick Foster, Alex Serb. 2025-12-16. An Energy-Efficient Adiabatic Capacitive Neural Network Chip. https://arxiv.org/abs/2512.14642

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