SyncNN: Evaluating and Accelerating Spiking Neural Networks on FPGAs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Compared to conventional artificial neural networks, spiking neural networks (SNNs) are more biologically plausible and require less computation due to their event-driven nature of spiking neurons. However, the default asynchronous execution of SNNs also poses great challenges to accelerate their performance on FPGAs. In this work, we present a novel synchronous approach for rate-encoding-based SNNs, which is more hardware friendly than conventional asynchronous approaches. We first quantitatively evaluate and mathematically prove that the proposed synchronous approach and asynchronous implementation alternatives of rate-encoding-based SNNs are similar in terms of inference accuracy, and we highlight the computational performance advantage of using SyncNN over an asynchronous approach. We also design and implement the SyncNN framework to accelerate SNNs on Xilinx ARM-FPGA SoCs in a synchronous fashion. To improve the computation and memory access efficiency, we first quantize the network weights to 16-bit, 8-bit, and 4-bit fixed-point values with the SNN-friendly quantization techniques. Moreover, we encode only the activated neurons by recording their positions and corresponding number of spikes to fully utilize the event-driven characteristics of SNNs, instead of using the common binary encoding (i.e., 1 for a spike and 0 for no spike). For the encoded neurons that have dynamic and irregular access patterns, we design parameterized compute engines to accelerate their performance on the FPGA, where we explore various parallelization strategies and memory access optimizations. Our experimental results on multiple Xilinx ARM-FPGA SoC boards demonstrate that our SyncNN is scalable to run multiple networks, such as LeNet, Network in Network, and VGG, on various datasets such as MNIST, SVHN, and CIFAR-10. SyncNN not only achieves competitive accuracy (99.6%) but also achieves state-of-the-art performance (13,086 frames per second) for the MNIST dataset. Finally, we compare the performance of SyncNN with conventional CNNs using the Vitis AI and find that SyncNN can achieve similar accuracy and better performance compared to Vitis AI for image classification using small networks.
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.
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 | 0.000 | 0.000 |
| 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 it