Evidence map›Paper›PMID 41113439›Full record

ReviewFrontiers in neuroscience2025

A comparative review of deep and spiking neural networks for edge AI neuromorphic circuits.

Pietro M Ferreira, Siqi Wang, Yueyuan Gao, Aziz Benlarbi-Delai

Abstract readReview
In one paragraph

Review in Frontiers in neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

4 authors.

Pietro M FerreiraUniversity Savoie Mont Blanc, University Grenoble Alpes, Grenoble INP, CNRS, CROMA, Chambéry, France.
Siqi WangSorbonne Université, CNRS, Laboratory de Génie Électrique et Électronique de Paris, Paris, France.
Yueyuan GaoFaculty of Materials for Energy, Shimane University, Matsue, Japan.
Aziz Benlarbi-DelaiSorbonne Université, CNRS, Laboratory de Génie Électrique et Électronique de Paris, Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Edge AI implements neural networks directly in electronic circuits, using either deep neural networks (DNNs) or neuromorphic spiking neural networks (SNNs). DNNs offer high accuracy and easy-to-use tools but are computationally intensive and consume significant power. SNNs utilize bio-inspired, event-driven architectures that can be significantly more energy-efficient, but they rely on less mature training tools. This review surveys digital and analog edge-AI implementations, outlining device architectures, neuron models, and trade-offs in energy (J/OP), area (μm

Indexed as

deep neural networks (DNN)edge AIenergy efficiencyneuromorphic circuitsspiking neural network (SNN)

Identifiers

PMID41113439
PMCPMC12528140

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.