Let Coarse-Grained Resources Be Shared: Mapping Entire Neural Networks on FPGAs
Why this work is in the frame
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Bibliographic record
Abstract
Traditional High-Level Synthesis (HLS) provides rapid prototyping of hardware accelerators without coding with Hardware Description Languages (HDLs). However, such an approach does not well support allocating large applications like entire deep neural networks on a single Field Programmable Gate Array (FPGA) device. The approach leads to designs that are inefficient or do not fit into FPGAs due to resource constraints. This work proposes to shrink generated designs by coarse-grained resource control based on function sharing in functional Intermediate Representations (IRs). The proposed compiler passes and rewrite system aim at producing valid design points and removing redundant hardware. Such optimizations make fitting entire neural networks on FPGAs feasible and produce competitive performance compared to running specialized kernels for each layer.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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