Bibliographic record
Abstract
The advancement in spiking neural networks (SNNs) provides a promising and alternative approach to conventional artificial neural networks (ANNs) with higher energy efficiency. However, the significant requirements on memory usage presents a performance bottleneck on resource constrained devices. Inspired by the notion of binarized neural networks (BNNs), we incorporate the design principles in BNNs into that of SNNs to reduce the stringent resource requirements. Specifically, the weights are binarized to 1 and -1 for implementing the functions of excitatory and inhibitory synapses. Hence, the proposed design is referred to as a weight-binarized spiking neural network (WB-SNN). In the WB-SNN, only one bit is used for the weight or a spike; for the latter, 1 and 0 indicate a spike and no spike, respectively. A priority encoder is used to identify the index of an active neuron as a basic unit to construct the WB-SNN. We further design a fully connected neural network that consists of an input layer, an output layer, and fully connected layers of different sizes. A counter is utilized in each neuron to complete the accumulation of weights. The WB-SNN design is validated by using a multi-layer perceptron on the MNIST dataset. Hardware implementations on FPGAs show that the WB-SNN attains a significant saving of memory with only a limited accuracy loss compared with its SNN and BNN counterparts.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".