Neuromorphic Hardware for Always-On Sensing: A Benchmark of Energy Per Inference Against Conventional Devices

Authors

  • Evangelia Christoforou Department of Electronic Engineering, Tylliria Institute of Advanced Technology, Kato Pyrgos, Cyprus Author
  • Dionisia Iriondo Department of Instrumentation, Cinco Villas Centre for Emerging Materials, Ejea, Spain Author
  • Iarla Hourigan Department of Semiconductor Engineering, Barrow Valley Centre for Emerging Materials, Athy, Ireland Author

DOI:

https://doi.org/10.5281/zenodo.22673077

Keywords:

Neuromorphic Hardware for Always-On Sensing; Next-generation R&D; Evidence synthesis; Reproducibility; Research methodology

Abstract

There is real interest in neuromorphic hardware for always-on sensing right now, and real uncertainty about exactly where it
delivers the most value. We pull together the current evidence for neuromorphic hardware for always-on sensing, covering the
methodological, practical, regulatory and cost angles, with an eye on where practice has genuinely settled and where engineers
and reviewers are still largely working it out for themselves. Selective, context-specific adoption looks better supported by the
evidence gathered here than either broad rollout or blanket avoidance.

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Published

2024-02-28

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Vol. 3 No. 1 (2024): vol 3 issue 1 2024

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