Reconfigurable Digital FPGA Implementations for Neuromorphic Computing: A Survey on Recent Advances and Future Directions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it