pith. sign in

arxiv: 2604.22179 · v1 · submitted 2026-04-24 · 💻 cs.AR

Hardware-Software Co-Design for Event-Driven SNN Deployment on Low-Cost Neuromorphic FPGAs

Jiwoon Lee , Souvik Chakraborty , Syed Bahauddin Alam , Cheolsoo Park This is my paper

Pith reviewed 2026-05-08 09:46 UTC · model grok-4.3

classification 💻 cs.AR
keywords spiking neural networksFPGA deploymenthardware-software co-designneuromorphic hardwaretime-to-first-spikeevent-driven processingMNIST classification
0
0 comments X

The pith

A single exported artifact transfers PyTorch SNN definitions to event-driven FPGA hardware while preserving exact software semantics and results.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a hardware-software co-design that lets researchers define spiking neural networks in PyTorch and deploy them directly to low-cost FPGAs for event-driven execution. One artifact bundles weights, thresholds, connectivity, and time-to-first-spike decoding data so the identical model runs in a software reference and on the board without modification. On a 10-class MNIST classifier the routed 80 MHz FPGA design reaches 87.40 percent accuracy and produces identical outputs to software on every one of the 10,000 test images. The hardware path shows 0.1375 microseconds latency per image and roughly 31.6 nanojoules dynamic energy, with separate accelerator and system-level measurements against GPU and CPU baselines.

Core claim

The framework exports a single artifact containing weights, thresholds, connectivity descriptors, and grouped TTFS metadata from software to board execution. The artifact is reused unchanged by both the software reference and the FPGA runtime. A 10-class MNIST TTFS classifier in the routed 80 MHz design achieves 87.40 percent accuracy and matches the software reference on all 10,000 test images while delivering 0.1375 microseconds service latency per image and an estimated 31.6 nanojoules dynamic energy.

What carries the argument

The single exported artifact that bundles weights, thresholds, connectivity descriptors, and TTFS decoding metadata and is reused unchanged by software and hardware.

If this is right

  • Low-cost FPGAs become practical targets for PyTorch-defined SNNs with deterministic, reproducible results.
  • Event-driven hardware can deliver sub-microsecond latency and nanojoule energy for classification tasks.
  • Scope-aware measurement separates accelerator-only performance from full system energy and latency.
  • Software-defined models gain a direct path to neuromorphic hardware without separate hardware-first redesign.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same artifact approach could simplify integration of SNNs into existing PyTorch training pipelines without forcing researchers to maintain parallel hardware descriptions.
  • Energy and latency numbers suggest the method may scale to other small-footprint sensor or edge tasks if the artifact export remains compact.
  • Direct comparison with matched GPU and CPU baselines provides a template for evaluating future low-cost neuromorphic platforms on the same workloads.

Load-bearing premise

The exported artifact preserves full SNN semantics from software to hardware with no timing discrepancies, quantization effects, or non-deterministic behavior introduced during FPGA synthesis and execution.

What would settle it

Execute the identical 10,000 MNIST test images on both the software reference and the FPGA board and check whether every classification and every spike timing match exactly; any difference would show semantic loss.

Figures

Figures reproduced from arXiv: 2604.22179 by Cheolsoo Park, Jiwoon Lee, Souvik Chakraborty, Syed Bahauddin Alam.

Figure 1
Figure 1. Figure 1: Overview of the proposed hardware-software co-design path. The same deployment artifact drives both view at source ↗
Figure 2
Figure 2. Figure 2: System-path latency terms of the deployed workflow. The figure isolates software reference evaluation, spike view at source ↗
Figure 3
Figure 3. Figure 3: Input sparsity tolerance of the deployed classifier. The curve shows gradual degradation under controlled view at source ↗
read the original abstract

Low-cost FPGA platforms can broaden access to neuromorphic systems research, but current spiking neural network (SNN) workflows remain divided between hardware-first implementations, which are difficult to integrate with PyTorch-style development, and software-first frameworks, which often stop at simulation or GPU execution. This paper presents a semantics-preserving hardware-software co-design framework for the deterministic deployment of PyTorch-defined SNNs to event-driven FPGA execution. A single exported artifact carries weights, thresholds, connectivity descriptors, and grouped time-to-first-spike (TTFS) decoding metadata from software definition to board execution and is reused unchanged by both the software reference and the board runtime. A 10-class MNIST TTFS classifier implemented in the routed 80 MHz design achieves 87.40\% accuracy and matches the software reference on all 10,000 test images. The programmable-logic path delivers a service latency of 0.1375 {\mu}s/image and an estimated dynamic energy of 31.6 nJ/image, while scope-aware comparisons with matched GPU and CPU baselines keep accelerator-only and system-level measurements distinct. These results show that low-cost event-driven FPGA hardware can provide a direct and reproducible software-to-board path for software-defined SNN models.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The paper claims to provide a semantics-preserving hardware-software co-design framework that allows PyTorch-defined SNNs to be deployed deterministically to event-driven execution on low-cost FPGAs via a single exported artifact containing all necessary model parameters and metadata. It demonstrates this with a 10-class MNIST time-to-first-spike (TTFS) classifier implemented on an 80 MHz FPGA design, reporting 87.40% accuracy that exactly matches the software reference across all 10,000 test images, along with a service latency of 0.1375 μs per image and estimated dynamic energy of 31.6 nJ per image, while distinguishing accelerator and system-level metrics against GPU and CPU baselines.

Significance. Should the semantics-preservation property hold under rigorous verification, the framework would significantly lower the barrier to neuromorphic hardware experimentation by enabling direct, reproducible transitions from standard software SNN models to efficient FPGA implementations. The concrete latency and energy figures, combined with full test-set matching and baseline comparisons, provide a strong practical demonstration of the approach's potential for accessible neuromorphic systems research.

major comments (1)
  1. [Abstract and Results section on MNIST evaluation] The assertion that the exported artifact preserves SNN semantics is load-bearing for the central contribution. However, the supporting evidence is limited to identical final classifications on the 10,000 test images (Abstract). This does not establish that spike timings, event ordering, or membrane potential dynamics are preserved in the FPGA path, as synthesis artifacts, quantization, or grouped TTFS handling could change internal behavior without altering the output class. Additional verification, such as logging internal spike events or comparing membrane traces, would be required to substantiate the claim.
minor comments (1)
  1. [Abstract] The abstract includes raw LaTeX fragments (e.g., {mu}s); these should be properly formatted in the published version for readability.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and for recognizing the potential of the framework to lower barriers to neuromorphic hardware experimentation. We address the major comment below.

read point-by-point responses

  1. Referee: [Abstract and Results section on MNIST evaluation] The assertion that the exported artifact preserves SNN semantics is load-bearing for the central contribution. However, the supporting evidence is limited to identical final classifications on the 10,000 test images (Abstract). This does not establish that spike timings, event ordering, or membrane potential dynamics are preserved in the FPGA path, as synthesis artifacts, quantization, or grouped TTFS handling could change internal behavior without altering the output class. Additional verification, such as logging internal spike events or comparing membrane traces, would be required to substantiate the claim.

    Authors: We agree that matching final classifications on the full test set, while strong evidence of functional equivalence for a deterministic system, does not by itself confirm preservation of internal spike timings, event orderings, or membrane potential dynamics. The manuscript's semantics-preserving claim rests on the single shared artifact carrying identical parameters and metadata together with a direct hardware mapping that avoids quantization and other semantic-altering transformations. To address the referee's concern, the revised manuscript will incorporate additional verification: we will log and compare internal spike events (including timings and ordering) between the software reference and FPGA execution for a representative subset of test images, along with a brief discussion of how the grouped TTFS decoding is implemented identically in both paths. revision: yes

Circularity Check

0 steps flagged

No significant circularity; claims rest on direct empirical matching

full rationale

The paper presents a hardware-software co-design framework whose core result is an empirical side-by-side verification: a single exported artifact produces identical 10-class classifications on all 10,000 MNIST test images in both the PyTorch reference and the routed 80 MHz FPGA implementation, with reported accuracy of 87.40%. This match is an external, falsifiable outcome rather than a quantity derived from itself. No equations, parameters, or uniqueness theorems are shown to reduce by construction to prior fits or self-citations; latency and energy figures are measured post-synthesis quantities. The derivation chain therefore remains self-contained against the provided benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based on abstract only; no explicit free parameters, axioms, or invented entities are described. The work assumes standard FPGA synthesis tools and SNN model semantics are preserved without additional postulates.

pith-pipeline@v0.9.0 · 5533 in / 1084 out tokens · 38287 ms · 2026-05-08T09:46:37.090352+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

17 extracted references · 3 canonical work pages

  1. [1]

    Brown, and Steve B

    Eustace Painkras, Luis Plana, Jim Garside, Steve Temple, Francesco Galluppi, Cameron Patterson, David Lester, Andrew D. Brown, and Steve B. Furber. Spinnaker: A 1-W 18-core system-on-chip for massively parallel neural network simulation.IEEE Journal of Solid-State Circuits, 48(8):1943–1953, 2013. doi: 10.1109/JSSC.2013. 2259038

    work page doi:10.1109/jssc.2013 1943

  2. [2]

    High dynamic range digital neuron core with time-embedded floating-point arithmetic.IEEE Transactions on Circuits and Systems I: Regular Papers, 70(1):290–301, 2022

    Jongkil Park, YeonJoo Jeong, Jaewook Kim, Suyoun Lee, Joon Young Kwak, Jong-Keuk Park, and Inho Kim. High dynamic range digital neuron core with time-embedded floating-point arithmetic.IEEE Transactions on Circuits and Systems I: Regular Papers, 70(1):290–301, 2022. 5 APREPRINT- APRIL27, 2026 0 25 50 75 Input spike drop ratio (%) 50 60 70 80 90 Accuracy (...

    2022

  3. [3]

    High-density digital neuromorphic processor with high-precision neural and synaptic dynamics and temporal acceleration

    Jongkil Park, YeonJoo Jeong, Jaewook Kim, Suyoun Lee, Joon Young Kwak, Jong-Keuk Park, and Inho Kim. High-density digital neuromorphic processor with high-precision neural and synaptic dynamics and temporal acceleration. In2024 IEEE 6th International Conference on AI Circuits and Systems (AICAS), pages 322–326. IEEE, 2024

    2024

  4. [4]

    Design-Efficient Approximate Multiplication Circuits Through Partial Product Perforation

    Daniel Neil and Shih-Chii Liu. Minitaur, an event-driven fpga-based spiking network accelerator.IEEE Transactions on V ery Large Scale Integration (VLSI) Systems, 22(12):2621–2628, 2014. doi: 10.1109/TVLSI. 2013.2294916

    work page doi:10.1109/tvlsi 2014

  5. [5]

    Loihi: A neuromorphic manycore processor with on-chip learning.IEEE Micro, 38(1):82–99, 2018

    Mike Davies, Narayan Srinivasa, Tsung-Han Lin, Gautham Chinya, Yongqiang Cao, Sriharsha Choday, George Dimou, Prasad Joshi, Nabil Imam, Shweta Jain, Yuchen Liao, Chung-Kuan Lin, Andreas Lines, Ruokun Liu, Deepak Mathaikutty, Steve McCoy, Arnab Paul, Jonathan Tse, Gururaj Venkataramanan, Yat-Hang Weng, Andreas Wild, Yoon Yang, and Hong Wang. Loihi: A neuro...

    work page doi:10.1109/mm.2018.112130359 2018

  6. [6]

    Lava software framework.https://lava-nc.org/, 2021

    Intel. Lava software framework.https://lava-nc.org/, 2021. Accessed: 2026-04-07

    2021

  7. [7]

    Pytorch: An imperative style, high-performance deep learning library.Advances in Neural Information Processing Systems, 32, 2019

    Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, high-performance deep learning library.Advances in Neural Information Processing Systems, 32, 2019

    2019

  8. [8]

    Spikingjelly: An open-source machine learning infrastructure platform for spike-based intelligence.Science Advances, 9(40):eadi1480, 2023

    Wei Fang, Yanqi Chen, Jianhao Ding, Zhaofei Yu, Timothée Masquelier, Ding Chen, Liwei Huang, Huihui Zhou, Guoqi Li, and Yonghong Tian. Spikingjelly: An open-source machine learning infrastructure platform for spike-based intelligence.Science Advances, 9(40):eadi1480, 2023

    2023

  9. [9]

    Bindsnet: A machine learning-oriented spiking neural networks library in python.Frontiers in Neuroinformatics, 12:89, 2018

    Hananel Hazan, Daniel J Saunders, Hassaan Khan, Devdhar Patel, Darpan T Sanghavi, Hava T Siegelmann, and Robert Kozma. Bindsnet: A machine learning-oriented spiking neural networks library in python.Frontiers in Neuroinformatics, 12:89, 2018

    2018

  10. [10]

    Spyketorch: Efficient simulation of convolutional spiking neural networks with at most one spike per neuron.Frontiers in Neuroscience, 13:625, 2019

    Milad Mozafari, Mohammad Ganjtabesh, Abbas Nowzari-Dalini, and Timothée Masquelier. Spyketorch: Efficient simulation of convolutional spiking neural networks with at most one spike per neuron.Frontiers in Neuroscience, 13:625, 2019. 6 APREPRINT- APRIL27, 2026

    2019

  11. [11]

    T2fsnn: Deep spiking neural networks with time-to-first-spike coding

    Seongsik Park, Seijoon Kim, Byunggook Na, and Sungroh Yoon. T2fsnn: Deep spiking neural networks with time-to-first-spike coding. In2020 57th ACM/IEEE design automation conference (DAC), pages 1–6. IEEE, 2020

    2020

  12. [12]

    Gradient based learning applied to docu- ment recognition.Proceedings of IEEE, 86(11):2278–2324, 1998

    Yann Le Cun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient based learning applied to docu- ment recognition.Proceedings of IEEE, 86(11):2278–2324, 1998. URL http://leon.bottou.org/papers/ lecun-98h

    1998

  13. [13]

    Fast and energy-efficient neuromorphic deep learning with first-spike times.Nature Machine Intelligence, 3(9):823–835, 2021

    Julian Göltz, Laura Kriener, Andreas Baumbach, Sebastian Billaudelle, Oliver Breitwieser, Benjamin Cramer, Do- minik Dold, Akos Ferenc Kungl, Walter Senn, Johannes Schemmel, et al. Fast and energy-efficient neuromorphic deep learning with first-spike times.Nature Machine Intelligence, 3(9):823–835, 2021

    2021

  14. [14]

    Neuronal competition groups with supervised stdp for spike-based classification.Advances in Neural Information Processing Systems, 37:106295–106314, 2024

    Gaspard Goupy, Pierre Tirilly, and Ioan Marius Bilasco. Neuronal competition groups with supervised stdp for spike-based classification.Advances in Neural Information Processing Systems, 37:106295–106314, 2024

    2024

  15. [15]

    Unsupervised learning of digit recognition using spike-timing-dependent plasticity.Frontiers in Computational Neuroscience, 9:149773, 2015

    Peter U Diehl and Matthew Cook. Unsupervised learning of digit recognition using spike-timing-dependent plasticity.Frontiers in Computational Neuroscience, 9:149773, 2015

    2015

  16. [16]

    Advanced Micro Devices, Inc., 2025

    Vivado Design Suite User Guide: Power Analysis and Optimization (UG907). Advanced Micro Devices, Inc., 2025. URL https://docs.amd.com/r/en-US/ug907-vivado-power-analysis-optimization/Output-Tab . Version 2025.2

    2025

  17. [17]

    Advanced Micro Devices, Inc., 2022

    7 Series FPGAs and Zynq-7000 SoC XADC Dual 12-Bit 1 MSPS Analog-to-Digital Converter User Guide (UG480). Advanced Micro Devices, Inc., 2022. URL https://docs.amd.com/r/en-US/ug480_7Series_ XADC/Reference-Inputs-VREFP-and-VREFN. Revision 1.11. 7

    2022