Hardware Oriented Evolutionary Spiking Neural Network
Bibliographic record
Abstract
With the ever-increasing complications of Artificial Neural Networks (ANNs), the power and resource costs demand more attention in the design process. Spiking Neural Networks (SNN) are a type of neural networks that promise simpler models which can be power and cost effective. However, these networks have complications regarding training and adaptation. Genetic algorithms are another nature inspired technique of finding the best solutions to a problem, advantageous in terms of ease of implementation and better control over network parameters. This work proposes a method of training SNNs using genetic algorithms for a navigational problem and optimize network parameters to reduce power and computational costs. GA results can be translated into FPGA implementable networks, using the LIF neuron model with optimized arithmetic and logical units, with 86 percent classification accuracy, 34.33 and 12.81 percent lower LUT and FF utilization respectively, and 397mW lower power usage compared to a network trained using backpropagation.
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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 | 0.000 |
| 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".