Reconfigurable Digital FPGA Implementations for Neuromorphic Computing: A Survey on Recent Advances and Future Directions
Bibliographic record
Abstract
Neuromorphic computing represents hardware and software paradigms that emulate neural brain functionalities. Spiking neural networks (SNNs) are a promising brain-inspired computing approach to achieve power efficiency through event-driven processing using discrete asynchronous spikes, making them particularly effective for spatiotemporal data processing. The complex computational nature of SNNs requires intensive calculations and specialized algorithms to ensure accurate performance across different tasks. Hardware accelerators for neuromorphic computing, particularly for SNN implementations, have emerged primarily through field programmable gate arrays (FPGAs) and application-specific integrated circuits (ASICs). FPGAs are especially attractive for neuromorphic computing due to their flexibility, stability, programmability, reconfigurability, and rapid time to market. This research explores top-tier and well-known journal articles from the IEEE Xplore digital library and the Google Scholar databases including IEEE, ACM, Frontiers, Elsevier, Springer, MDPI, Wiley, arXiv, and Nature publishers. In this survey, various energy-efficient and high-performance FPGA implementations of spiking neurons and SNNs are reviewed. The accuracy rates of the implemented SNNs on different applications are investigated. Also, digital hardware optimization techniques for reconfigurable implementations are discussed. The synthesis results from the presented implementations are reported and compared in terms of cost (referring to utilized resources such as Registers/FFs, LUTs, Multipliers, DSP blocks, and Block RAMs), speed, and power/energy consumption. The survey concludes with recommendations for future research directions in FPGA-based neuromorphic computing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".