You Cannot Improve What You Do not Measure
Bibliographic record
Abstract
Recently, deep learning (DL) has become best-in-class for numerous applications but at a high computational cost that necessitates high-performance energy-efficient acceleration. The reconfigurability of FPGAs is appealing due to the rapid change in DL models but also causes lower performance and area-efficiency compared to ASICs. In this article, we implement three state-of-the-art computing architectures (CAs) for convolutional neural network (CNN) inference on FPGAs and ASICs. By comparing the FPGA and ASIC implementations, we highlight the area and performance costs of programmability to pinpoint the inefficiencies in current FPGA architectures. We perform our experiments using three variations of these CAs for AlexNet, VGG-16 and ResNet-50 to allow extensive comparisons. We find that the performance gap varies significantly from 2.8× to 6.3×, while the area gap is consistent across CAs with an 8.7 average FPGA-to-ASIC area ratio. Among different blocks of the CAs, the convolution engine, constituting up to 60% of the total area, has a high area ratio ranging from 13 to 31. Motivated by our FPGA vs. ASIC comparisons, we suggest FPGA architectural changes such as increasing DSP block count, enhancing low-precision support in DSP blocks and rethinking the on-chip memories to reduce the programmability gap for DL applications.
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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 | 0.024 | 0.155 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.034 | 0.026 |
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".