Pith Number

pith:3222RQAV

pith:2026:3222RQAVKRKB2FBR4XBRRIIMGH

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Scalable neuromorphic computing from autonomous spiking dynamics in a clockless reconfigurable chip

Damien Rontani, Eric Oliveira Gomes

Clockless asynchronous circuits on standard FPGAs generate autonomous spiking dynamics that solve machine-learning tasks at competitive accuracy with low power.

arxiv:2605.16114 v1 · 2026-05-15 · cs.NE · cs.LG

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

We demonstrate competitive performance for an audio classification task with spike-based encoding and high-speed processing. Power consumption is significantly lower than traditional digital implementations; this makes our approach an efficient alternative that bridges the gap to dedicated analog neuromorphic systems without the need for specialized hardware design.

C2weakest assumption

That autonomous spiking dynamics arising from the time-continuous evolution of clockless digital circuits on commercial FPGAs can be configured via synaptic weights to solve machine-learning tasks at competitive accuracy without hidden costs in scalability or stability.

C3one line summary

Clockless FPGA circuits produce autonomous spiking neuron networks that achieve competitive audio classification accuracy with significantly lower power than conventional digital implementations.

References

58 extracted · 58 resolved · 5 Pith anchors

[1] Ostrau, C., Klarhorst, C., Thies, M. & Rückert, U. Benchmarking Neuromorphic Hardware and Its Energy Expenditure. Front. Neurosci.16, 873935, DOI: 10.3389/fnins.2022.873935 (2022) 2022 · doi:10.3389/fnins.2022.873935

[2] Pal, A.et al.An ultra energy-efficient hardware platform for neuromorphic computing enabled by 2D-TMD tunnel-FETs. Nat. Commun.15, 3392, DOI: 10.1038/s41467-024-46397-3 (2024) 2024 · doi:10.1038/s41467-024-46397-3

[3] Jaeger, H. & Haas, H. Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication. Science304, 78–80, DOI: 10.1126/science.1091277 (2004) 2004 · doi:10.1126/science.1091277

[4] Real-time computing without stable states: A new framework for neural computation based on perturbations.Neural Computation, 14(11): 2531–2560 2002 · doi:10.1162/089976602760407955

[5] Reservoir computing approaches to recurrent neural network training 2009 · doi:10.1016/j.cosrev.2009.03.005

Formal links

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Receipt and verification

First computed	2026-05-20T00:01:53.467588Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

deb5a8c01554541d1431e5c318a10c31dac9397bb7f4453c8883b073a20c7856

Aliases

arxiv: 2605.16114 · arxiv_version: 2605.16114v1 · doi: 10.48550/arxiv.2605.16114 · pith_short_12: 3222RQAVKRKB · pith_short_16: 3222RQAVKRKB2FBR · pith_short_8: 3222RQAV

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/3222RQAVKRKB2FBR4XBRRIIMGH \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: deb5a8c01554541d1431e5c318a10c31dac9397bb7f4453c8883b073a20c7856

Canonical record JSON

{
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    "abstract_canon_sha256": "154d80252cc3fb089923f96d76e19eb728569bf9552d93566a1ddf683719211b",
    "cross_cats_sorted": [
      "cs.LG"
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.NE",
    "submitted_at": "2026-05-15T15:58:38Z",
    "title_canon_sha256": "bf5ecda3e2b28e8b52a7f19365ef7bae43c915a34353acd4dbdf3a8ed7f3f02b"
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