Customized Development and Hardware Optimization of a Fully-Spiking SNN for EEG-Based Seizure Detection
Bibliographic record
Abstract
We present a customized approach in development of a fully-spiking neural networks (SNNs) tailored for energy-efficient data-driven signal processing in implantable brain neural interfaces. The importance of a customized design lies in its ability to optimize hardware and energy efficiency while maintaining high classification performance. Our approach allows for the customization of key parameters, including quantization resolution of weights and biases, encoding scheme, encoder placement, temporal resolution, neuron type, and internal parameters such as threshold value, resetting process, and refractory period. We demonstrated the efficacy of this customization in improving hardware and energy efficiency through model development, software-based training and testing, and subsequent synthesis using Verilog RTL on FPGAs and ASIC implementation. Performance evaluation using the CHB-MIT dataset showed an average sensitivity of $\mathbf{9 2. 2 \%}$ and specificity of $\mathbf{9 7. 3 \%}$ for seizure detection. The synthesis reports provided insights into the memory, computation, and energy requirements for hardware implementation, highlighting the efficiency and effectiveness of our approach. Our results show that SNN models leads to only 1% drop of sensitivity compared with a 32-bit real-value resolution SCNN model, while offering more than 4 times improvement in memory efficiency.
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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".