Spike Timing Dependent Gradient for Direct Training of Fast and Efficient Binarized Spiking Neural Networks
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
Spiking neural networks (SNNs) are well-suited for neuromorphic hardware due to their biological plausibility and energy efficiency. These networks utilize sparse, asynchronous spikes for communication and can be binarized. However, the training of such networks presents several challenges due to their non-differentiable activation function and binarized inter-layer data movement.The well-established backpropagation through time (BPTT) algorithm used to train SNNs encounters notable difficulties because of its substantial memory consumption and extensive computational demands. These limitations restrict its practical utility in real-world scenarios. Therefore, effective techniques are required to train such networks efficiently while preserving accuracy. In this paper, we propose Binarized Spike Timing Dependent Gradient (BSTDG), a novel method that utilizes presynaptic and postsynaptic timings to bypass the non-differentiable gradient and the need of BPTT. Additionally, we employ binarized weights with a threshold training strategy to enhance energy savings and performance. Moreover, we exploit latency/temporal-based coding and the Integrate-and-Fire (IF) model to achieve significant computational advantages. We evaluate the proposed method on Caltech101 Face/Motorcycle, MNIST, Fashion-MNIST, and Spiking Heidelberg Digits. The results demonstrate that the accuracy attained surpasses that of existing BSNNs and single-spike networks under the same structure. Furthermore, the proposed model achieves up to 30× speedup in inference and effectively reduces the number of spikes emitted in the hidden layer by 50% compared to previous works.
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 | 0.000 |
| 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 it