Efficient event-driven approach using synchrony processing for hardware spiking neural networks
Why this work is in the frame
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Bibliographic record
Abstract
Current digital hardware implementations of spiking neural networks usually focus on a time-driven architecture to process the large number of events that occur during a typical simulation. While this type of implementation is practical for simulating biologically accurate neurons, most systems using a simpler neuron model can benefit from an event-driven architecture. In such cases, significant performance improvements are theoretically possible. In practice, however, such implementations do not maximize the available computational power because finding the next event often involves serializing computations. In this paper, a hardware architecture that offers the efficiency of an event-driven algorithm while allowing parallel computations is developed. The architecture uses multiple pipelined processing elements to compute spikes in parallel and a novel comparator tree structure to find the next event in a large network efficiently. The resulting system can implement up to 131 072 neurons on a single FPGA (Xilinx Virtex-6 XC6VLX240T) and processes approximately 70 million spikes per second when using a 4-bank architecture clocked at 100 MHz.
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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