An Efficient FPGA-based Overlay Inference Architecture for Fully Connected DNNs
Bibliographic record
Abstract
Deep Neural Networks (DNNs) have gained significant popularity in several classification and regression applications. The massive computation and memory requirements of DNNs pose special challenges for FPGA implementation. Moreover, programming FPGAs requires hardware-specific knowledge that many machine-learning researchers do not possess. To make the power and versatility of FPGAs available to a wider DNN user community and to improve DNN design efficiency, we introduce a Single hidden layer Neural Network (SNN) multiplication-free overlay architecture with fully connected DNN-level performance. This FPGA inference overlay can be used for applications that are normally solved with fully connected DNNs. The overlay avoids the time needed to synthesize, place, route and regenerate a new bitstream when the application changes. The SNN overlay inputs and activations are quantized to power-of-two values, which allows utilizing shift units instead of multipliers. Since the overlay is a SNN, we fill the FPGA chip with the maximum possible number of neurons that can work in parallel in the hidden layer. On a ZYNQ-7000 ZC706 FPGA, it is thus possible to implement 2450 neurons in the hidden layer and 30 neurons in the output layer. We evaluate the proposed architecture on typical benchmark datasets and demonstrate higher throughput with respect to the state-of-the-art while achieving the same accuracy.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".