Toward Large Scale All-Optical Spiking Neural Networks
Bibliographic record
Abstract
Silicon Photonics is a promising technology to develop neuromorphic hardware accelerators. Most optical neural networks rely on wavelength division multiplexing (WDM), which calls for power hungry calibration to compensate for non-uniformity fabrication process and thermal variations of microring resonators (MRR). This imposes practical limits on neuromorphic photonic hardware since only a small number of synaptic connections per neuron can be implemented. As a result, the mapping of neural networks (NN) on a hardware platform require pruning of synaptic connections, which drastically affects the accuracy. In this work, we propose a method to efficiently map pre-trained NN on an all-optical spiking neural network (SNN), with the aim to optimize hardware utilization while minimizing accuracy loss. The method relies on weight partitioning and unrolling to reduce synaptic connections. The resulting neural networks are mapped on an architecture we propose, allowing to estimate accuracy and power consumption. Results show the capability of weight partitioning to implement a realistic NN while attaining 58% reduction in energy consumption compared with unrolling.
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.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".