ARXIV:2602.16442 · HARDWARE-ACCELERATED AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Hardware-accelerated graph neural networks: an alternative approach for neuromorphic event-based audio classification and keyword spotting on SoC FPGA

Q: What products could be built from this research?

The technology can be productized as a hardware chip or a module integrated into smart devices, enabling offline and real-time audio processing without high energy consumption.

Q: What are the practical use cases?

Create a low-power audio processing chip for smart home devices to recognize user commands quickly and efficiently.

Q: What industries could this research disrupt?

This technology can replace traditional microphone and GPU-based audio processing systems, offering more efficient, energy-saving solutions.

arXiv

Develop a low-power, low-latency hardware-accelerated event-graph neural network for real-time audio classification and keyword spotting on FPGA.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain Develop a low-power, low-latency hardware-accelerated event-graph neural network for real-time audio classification and keyword spotting on FPGA.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Develop a low-power, low-latency hardware-accelerated event-graph neural network for real-time audio classification and keyword spotting on FPGA. We present an FPGA implementation of event-graph neural networks for audio processing.

METHOD

Full abstract

As the volume of data recorded by embedded edge sensors increases, particularly from neuromorphic devices producing discrete event streams, there is a growing need for hardware-aware neural architectures that enable efficient, low-latency, and energy-conscious local processing. We present an FPGA implementation of event-graph neural networks for audio processing. We utilise an artificial cochlea that converts time-series signals into sparse event data, reducing memory and computation costs. Our architecture was implemented on a SoC FPGA and evaluated on two open-source datasets. For classification task, our baseline floating-point model achieves 92.7% accuracy on SHD dataset - only 2.4% below the state of the art - while requiring over 10x and 67x fewer parameters. On SSC, our models achieve 66.9-71.0% accuracy. Compared to FPGA-based spiking neural networks, our quantised model reaches 92.3% accuracy, outperforming them by up to 19.3% while reducing resource usage and latency. For SSC, we report the first hardware-accelerated evaluation. We further demonstrate the first end-to-end FPGA implementation of event-audio keyword spotting, combining graph convolutional layers with recurrent sequence modelling. The system achieves up to 95% word-end detection accuracy, with only 10.53 microsecond latency and 1.18 W power consumption, establishing a strong benchmark for energy-efficient event-driven KWS.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. As the volume of data recorded by embedded edge sensors increases, particularly from neuromorphic devices producing discrete event streams, there is a growing need…

WHY NOW

Hardware-Accelerated AI moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainDevelop a low-power, low-latency hardware-accelerated event-graph neural network for real-time audio classification and keyword spotting on FPGA.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develop a low-power, low-latency hardware-accelerated event-graph neural network for real-time audio classification and keyword spotting on FPGA.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2602.16442 · HARDWARE-ACCELERATED AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Hardware-accelerated graph neural networks: an alternative approach for neuromorphic event-based audio classification and keyword spotting on SoC FPGA

arXiv

Develop a low-power, low-latency hardware-accelerated event-graph neural network for real-time audio classification and keyword spotting on FPGA.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain Develop a low-power, low-latency hardware-accelerated event-graph neural network for real-time audio classification and keyword spotting on FPGA.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Hardware-Accelerated AI moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainDevelop a low-power, low-latency hardware-accelerated event-graph neural network for real-time audio classification and keyword spotting on FPGA.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develop a low-power, low-latency hardware-accelerated event-graph neural network for real-time audio classification and keyword spotting on FPGA.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2602.16442

Hardware-accelerated graph neural networks: an alternative approach for neuromorphic event-based audio classification and keyword spotting on SoC FPGA

Develop a low-power, low-latency hardware-accelerated event-graph neural network for real-time audio classification and keyword spotting on FPGA.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback51 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

7.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Hardware-Accelerated AI moved forward this cycle; last verified April 2026. Public score 7.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
Develop a low-power, low-latency hardware-accelerated event-graph neural network for real-time audio classification and keyword spotting on FPGA. We present an FPGA implementation of event-graph neural networks for audio processing.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
As the volume of data recorded by embedded edge sensors increases, particularly from neuromorphic devices producing discrete event streams, there is a growing need for hardware-aware neural architectures that enable efficient, low-latency, and energy-conscious local processing. We present an FPGA implementation of event-graph neural networks for audio processing.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. As the volume of data recorded by embedded edge sensors increases, particularly from neuromorphic devices producing discrete event streams, there is a growing need for hardware-aware neural architectures that enable efficient, low-latency, and energy-conscious local processing.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Hardware-Accelerated AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Hardware-Accelerated AI

Competitors

not indexed

Competitive landscape

Develop a low-power, low-latency hardware-accelerated event-graph neural network for real-time audio classification and keyword spotting on FPGA.

Segment

Hardware-Accelerated AI

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.16442 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(51)

Event-based Audio Prediction with Spectro-Temporal Event-Graphs

2025Lars Rafeldt, Thomas Mesquida et al.

Scaling Up Resonate-and-Fire Networks for Fast Deep Learning

2025Thomas E. Huber, Jules Lecomte et al.

Automatic Speech Recognition: A survey of deep learning techniques and approaches

2025Harsh Ahlawat, Naveen Aggarwal et al.

TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks

2024Prajna G. Malettira, Shubham Negi et al.

FPGA-Based Hardware Acceleration for Deep Learning in Mobile Robotics

2024Yasir Al-Ameri, Ming Nguyen et al.

Event-Based Vision on FPGAs - a Survey

2024T. Kryjak

Embedded Graph Convolutional Networks for Real-Time Event Data Processing on SoC FPGAs

2024K. Jeziorek, Piotr Wzorek et al.

High Accuracy and Low Latency Mixed Precision Neural Network Acceleration for TinyML Applications on Resource-Constrained FPGAs

2024Wei Soon Ng, Wangling Goh et al.

EvGNN: An Event-Driven Graph Neural Network Accelerator for Edge Vision

2024Yufeng Yang, Adrian Kneip et al.

Scalable Event-by-Event Processing of Neuromorphic Sensory Signals with Deep State-Space Models

2024Mark Schöne, Neeraj Mohan Sushma et al.

A Fully-Configurable Open-Source Software-Defined Digital Quantized Spiking Neural Core Architecture

2024Shadi Matinizadeh, Noah Pacik-Nelson et al.

17.8 0.4V 988nW Time-Domain Audio Feature Extraction for Keyword Spotting Using Injection-Locked Oscillators

2024A. Mostafa, Emmanuel Hardy et al.

DenRAM: Neuromorphic Dendritic Architecture with RRAM for Efficient Temporal Processing with Delays

2023Simone DAgostino, Filippo Moro et al.

TinyML Acoustic Classification using RAMAN Accelerator and Neuromorphic Cochlea

2023Adithya Krishna, H. Shankaranarayanan et al.

Learnable axonal delay in spiking neural networks improves spoken word recognition

2023Pengfei Sun, Yansong Chua et al.

Memory-Efficient Graph Convolutional Networks for Object Classification and Detection with Event Cameras

2023K. Jeziorek, A. Pinna et al.

Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings

2023Ilyass Hammouamri, Ismail Khalfaoui Hassani et al.

Low-Power Event-Driven Spectrogram Extractor for Multiple Keyword Spotting: A proof of concept

2023Soufiane Mourrane, Benoît Larras et al.

HUGNet: Hemi-Spherical Update Graph Neural Network applied to low-latency event-based optical flow

2023Thomas Dalgaty, Thomas Mesquida et al.

Are SNNs Really More Energy-Efficient Than ANNs? an In-Depth Hardware-Aware Study

2023Manon Dampfhoffer, T. Mesquida et al.

Showing 20 of 51 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkAt the Edge of the Heart: ULP FPGA-Based CNN for On-Device Cardiac Feature Extraction in Smart Health Sensors for Astronauts

7.0

Prior WorkSurrogates, Spikes, and Sparsity: Performance Analysis and Characterization of SNN Hyperparameters on Hardware

7.0

Prior WorkSurrogate Neural Architecture Codesign Package (SNAC-Pack)

7.0

Extension

Builds On ThisHeterogeneous SoC Integrating an Open-Source Recurrent SNN Accelerator for Neuromorphic Edge Computing on FPGA

4.0

Builds On ThisDendroNN: Dendrocentric Neural Networks for Energy-Efficient Classification of Event-Based Data

4.0

Builds On ThisAceleradorSNN: A Neuromorphic Cognitive System Integrating Spiking Neural Networks and DynamicImage Signal Processing on FPGA

3.0

Builds On ThisBridging Biological Hearing and Neuromorphic Computing: End-to-End Time-Domain Audio Signal Processing with Reservoir Computing

5.0

Builds On ThisELSA: An ELastic SNN Inference Architecture for Efficient Neuromorphic Computing

3.0

Builds On ThisHiAER-Spike Software-Hardware Reconfigurable Platform for Event-Driven Neuromorphic Computing at Scale

4.0

Builds On ThisDesign Rules for Extreme-Edge Scientific Computing on AI Engines

6.0

Commercially relevant

none indexed

Conflicting

none indexed

Owned Distribution

Subscribe to the weekly brief

Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2602.16442
Route: /paper/hardware-accelerated-graph-neural-networks-an-alternative-approach-for-neuromorphic-event-based-audio-classification-and
Active tab: read
Artifact: hardware-accelerated-graph-neural-networks-an-alternative-approach-for-neuromorphic-event-based-audio-classification-and

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/hardware-accelerated-graph-neural-networks-an-alternative-approach-for-neuromorphic-event-based-audio-classification-and/paper-pack
REST build passport API/api/v1/paper/hardware-accelerated-graph-neural-networks-an-alternative-approach-for-neuromorphic-event-based-audio-classification-and/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
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  "paper_id": "2b0527a1-90b6-4525-a731-b7c118614d92",
  "arxiv_id": "2602.16442",
  "canonical_route": "/paper/hardware-accelerated-graph-neural-networks-an-alternative-approach-for-neuromorphic-event-based-audio-classification-and",
  "active_tab": "synced from current hash by the drawer client",
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  "endpoints": {
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    "build_passport": "/api/v1/paper/hardware-accelerated-graph-neural-networks-an-alternative-approach-for-neuromorphic-event-based-audio-classification-and/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
  }
}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/hardware-accelerated-graph-neural-networks-an-alternative-approach-for-neuromorphic-event-based-audio-classification-and

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

Hardware-accelerated graph neural networks: an alternative approach for neuromorphic event-based audio classification and keyword spotting on SoC FPGA

Canonical ID hardware-accelerated-graph-neural-networks-an-alternative-approach-for-neuromorphic-event-based-audio-classification-and | Route /paper/hardware-accelerated-graph-neural-networks-an-alternative-approach-for-neuromorphic-event-based-audio-classification-and

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/hardware-accelerated-graph-neural-networks-an-alternative-approach-for-neuromorphic-event-based-audio-classification-and

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2602.16442"
  }
}

source_context

{
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  "mode": "paper",
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  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: Hardware-accelerated graph neural networks: an alternative approach for neuromorphic event-based audio classification and keyword spotting on SoC FPGA

/buildability/hardware-accelerated-graph-neural-networks-an-alternative-approach-for-neuromorphic-event-based-audio-classification-and

Watchwatch

Subject: Hardware-accelerated graph neural networks: an alternative approach for neuromorphic event-based audio classification and keyword spotting on SoC FPGA

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/hardware-accelerated-graph-neural-networks-an-alternative-approach-for-neuromorphic-event-based-audio-classification-and

Paper ref

hardware-accelerated-graph-neural-networks-an-alternative-approach-for-neuromorphic-event-based-audio-classification-and

arXiv id

2602.16442

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

e624bc8214a069134985b86a3b0ee69c416733c541da9faa2d2a68abda2d557c

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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Hardware-accelerated graph neural networks: an alternative approach for neuromorphic event-based audio classification and keyword spotting on SoC FPGA

Hardware-accelerated graph neural networks: an alternative approach for neuromorphic event-based audio classification and keyword spotting on SoC FPGA

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