A Cost-Effective FPGA-Based Approximate Multiplier for Machine Learning Acceleration
Bibliographic record
Abstract
This paper introduces a novel approximate multiplier tailored for FPGAs, with a primary focus on its application in hardware accelerators for deep machine learning computations. In deep learning, multipliers represent a substantial portion of the hardware complexity. The primary objective is to enhance hardware efficiency compared to exact multipliers while maintaining an acceptable level of accuracy, especially for inference tasks. The proposed multiplier employs a parallel architecture featuring a configurable set of approximate adders. The paper provides comprehensive insights into the design of both INT8 and UINT8 configurations of this multiplier. The evaluations encompass error analysis findings, hardware implementation results, and accuracy measurements for the inference of a selected set of benchmark deep learning models. The results illustrate that a chosen configuration of this multiplier can achieve a notable 9% reduction in overall LUT utilization while incurring only an average 0.28% and 0.06% reduction in inference accuracy before and after approximation-aware retraining, respectively.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".