FPGA Logic Block Architectures for Efficient Deep Learning Inference
Bibliographic record
Abstract
Reducing the precision of deep neural network (DNN) inference accelerators can yield large efficiency gains with little or no accuracy degradation compared to half or single precision floating-point by enabling more multiplication operations per unit area. A wide range of precisions fall on the pareto-optimal curve of hardware efficiency vs. accuracy with no single precision dominating, making the variable precision capabilities of FPGAs very valuable. We propose three types of logic block architectural enhancements and fully evaluate a total of six architectures that improve the area efficiency of multiplications and additions implemented in the soft fabric. Increasing the LUT fracturability and adding two adders to the ALM (4-bit Adder Double Chain architecture) leads to a 1.5× area reduction for arithmetic heavy machine learning (ML) kernels, while increasing their speed. In addition, this architecture also reduces the logic area of general applications by 6%, while increasing the critical path delay by only 1%. However, our highest impact option, which adds a 9-bit shadow multiplier to the logic clusters, reduces the area and critical path delay of ML kernels by 2.4× and 1.2×, respectively. These large gains come at a cost of 15% logic area increase for general 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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".