Heuristic & Expert-Guided Buffer Sizing for Neural Network Inference Applications on FPGAs
Bibliographic record
Abstract
A crucial step in implementing neural network engines on FPGAs is the sizing of intermediate buffers between the pipeline stages. Undersizing buffers can lead to throughput degradation or deadlocks, oversizing wastes valuable device resources. Typically, buffer sizing is performed using simulation, employing solvers or implementing a search algorithm. With neu-ral networks rapidly growing in size, these approaches become time consuming and negatively impact design iteration times. In this work, we show that, in the domain of neural network inference on FPGAs, it is possible to avoid costly simulations or solvers for buffer sizing and use instead a method that relies on analytic modeling and heuristics. We incorporate our method into the FINN compiler and deliver up to three orders of magnitude faster buffer sizing. At the same time, the resulting buffer sizes are up to 9x smaller for a wide range of example neural networks such as MobileNet-V1 and ResNet-50 with negligible degradation in throughput.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".