Spiking Neural Network Implementation on FPGA for Multiclass Classification
Bibliographic record
Abstract
Spiking Neural Network (SNN) is a particular Artificial Neural Networks (ANN) form. An SNN has similar features as an ANN, but an SNN has a different information system that will allow SNN to have higher energy efficiency than an ANN. This paper presents the design and implementation of an SNN on FPGA. The model of the SNN is designed to be lower power consumption than existing SNN models in the aspect of FPGA implementation and lower accuracy loss than the existing training method in the part of the algorithm. The coding scheme of the SNN model proposed in this paper is the rate coding scheme. This paper introduces a conversion method to directly map the trained parameters from ANN to SNN with negligible classification accuracy loss. Also, this paper demonstrates the technique of FPGA implementation for Spiking Exponential Function, Spiking SoftMax Function and Dynamic Adder Tree. This paper also presents the Time Division Component Reuse technic for lower resource utilization in the FPGA implementation of SNN. The proposed model has a power efficiency of 8841.7 frames per watt with negligible accuracy loss. The benchmark SNN model has a power efficiency of 337.6 frames per watt with an accuracy loss of 1.42 percent. The reference accuracy of the ANN model is 90.36 percent. For comparison, the specific model of the SNN has an accuracy of 90.39 percent.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".