Quantized Spiking Neural Networks on FPGA: An Application to Retinal Prosthetics
Bibliographic record
Abstract
We present an embedded Spiking Neural Network (SNN) in a Field-Programmable Gate Array (FPGA) for retinal prosthetic applications. Our primary goal is to minimize resource utilization, making the solution suitable for edge AI systems with neural-machine interfaces. The proposed quantized SNN minimizes computational resources for a power-efficient system. The PRANAS open-source software, which emulates retinal ganglion cells (RGCs), is used to generate a spiking dataset from popular MNIST database and the developed SNN is trained on it by the error backpropagation and surrogate gradient technique. We subsequently applied weight quantization on the trained SNN’s weights, using both 4-bit and 8-bit precision, to create a compact and power-efficient version of the SNN. The resulting accuracies after the 4-bit and 8-bit post-training quantizations are 83.2% and 87.2%, respectively. They show the potential of FPGA-based implementations of quantized SNNs to offer high-performance and energy-efficient solutions for retinal implants, thus answering their inherent hard power and area constraints.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".