Evidence map›Paper›PMID 42643922›Full record

ReviewFrontiers in neuroscience2026

A hardware-grounded energy taxonomy for comparing deep and Spiking Neural Network inference on edge platforms.

Mohamed El-Hafci, My Abdelouahed Sabri, Abdellah Aarab

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Frontiers in neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Mohamed El-HafciLaboratory of Engineering, Modeling and Systems Analysis (LIMAS), Faculty of Sciences Dhar El Mahraz (FSDM), Sidi Mohamed Ben Abdellah University, Fez, Morocco.
My Abdelouahed SabriLaboratory of Engineering, Modeling and Systems Analysis (LIMAS), Faculty of Sciences Dhar El Mahraz (FSDM), Sidi Mohamed Ben Abdellah University, Fez, Morocco.
Abdellah AarabLaboratory of Engineering, Modeling and Systems Analysis (LIMAS), Faculty of Sciences Dhar El Mahraz (FSDM), Sidi Mohamed Ben Abdellah University, Fez, Morocco.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Energy consumption is becoming one of the main constraints for neural network inference on edge devices, where compute, memory, and power budgets are tight. A large number of works already study the efficiency of Deep Neural Networks (DNNs), and a growing body of work does the same for Spiking Neural Networks (SNNs). However, comparing the two on equal terms is not straightforward because existing analyses rarely rely on a shared decomposition of inference energy linked to the actual hardware behavior. This Mini Review synthesizes hardware-aware studies into a unified taxonomy that decomposes inference energy into seven contributions: computation

Indexed as

CMOS power modelingedge AI acceleratorsenergy efficiency taxonomyHW/SW co-designneuromorphic engineeringSpiking Neural Networks

Identifiers

PMID42643922
PMCPMC13504384

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.