A hybrid edge classifier: combining tinyML-optimised CNN with RRAM-CMOS ACAM for energy-efficient inference

Woodward, K, Kanjo, E ORCID: https://orcid.org/0000-0002-1720-0661, Papandroulidakis, G, Agwa, S and Prodromakis, T, 2026. A hybrid edge classifier: combining tinyML-optimised CNN with RRAM-CMOS ACAM for energy-efficient inference. IEEE Transactions on Knowledge and Data Engineering. ISSN 1041-4347

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Abstract

In recent years, the development of smart edge computing systems to process information locally is on the rise. Many near-sensor machine learning (ML) approaches have been implemented to introduce accurate and energy efficient template matching operations in resource-constrained edge sensing systems, such as wearables. To introduce novel solutions that can be viable for extreme edge cases, hybrid solutions combining conventional and emerging technologies have started to be proposed. Deep Neural Networks (DNN) optimised for edge application alongside new approaches of computing (both device and architecture -wise) could be a strong candidate in implementing edge ML solutions that aim at competitive accuracy classification while using a fraction of the power of conventional ML solutions. In this work, we are proposing a hybrid software-hardware edge classifier aimed at the extreme edge near-sensor systems. The classifier consists of two parts: (i) an optimised digital tinyML network, working as a front-end feature extractor, and (ii) a back-end RRAM-CMOS analogue content addressable memory (ACAM), working as a final stage template matching system. The combined hybrid system exhibits a competitive trade-off in accuracy versus energy metric with Efront−end = 96.23nJ and Eback−end = 1.45nJ for each classification operation compared with 78.06 μJ for the original teacher model, representing a 792-fold reduction, making it a viable solution for extreme edge applications.

Item Type:	Journal article
Publication Title:	IEEE Transactions on Knowledge and Data Engineering
Creators:	Woodward, K., Kanjo, E., Papandroulidakis, G., Agwa, S. and Prodromakis, T.
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Date:	19 January 2026
ISSN:	1041-4347
Identifiers:	Number Type 10.1109/tkde.2026.3655717 DOI 2563067 Other
Rights:	© 2026 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Borcherds
Date Added:	06 Feb 2026 10:53
Last Modified:	06 Feb 2026 10:53
URI:	https://irep.ntu.ac.uk/id/eprint/55194

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