Efficient spiking neural network training and inference with reduced precision memory and computing
Bibliographic record
Abstract
In this study, reduced precision operations are investigated in order to improve the speed and energy efficiency of SNN implementation. Instead of using the 32‐bit single‐precision floating‐point format, small floating‐point format and fixed‐point format are used to represent SNN parameters and to perform SNN operations. The analyses are performed on the training and inference of a leaky integrate‐and‐fire model‐based SNN that is trained and used to classify the handwritten digits in MNIST database. The analysis results show that for SNN inference, the floating‐point format with 4‐bit exponent and 3‐bit mantissa or the fixed‐point format with 6‐bit integer and 7‐bit fraction can be used without any accuracy degradation. For training, a floating‐point format with 5‐bit exponent and 3‐bit mantissa or a fixed‐point format with 6‐bit integer and 10‐bit fraction can be used to obtain full accuracy. The proposed reduced precision formats can be used in SNN hardware accelerator design and the selection between floating‐point and fixed‐point can be determined by design requirements. A case study of SNN implementation on field‐programmable gate array device is performed. With reduced precision numerical formats, memory footprint, computing speed, and resource utilisation are improved. As a result, the energy efficiency of SNN implementation is also improved.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".