Efficient event-driven approach using synchrony processing for hardware spiking neural networks
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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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".