SparHiXcel: A Cost-Effective Sparsity-Aware Convolutional Accelerator
Bibliographic record
Abstract
High-efficiency deep learning (DL) hardware accelerators are essential for deploying DL technologies in resource-constrained applications. This paper presents a novel, scalable, and cost-effective sparsity-aware hardware accelerator for convo-lutional neural networks (CNNs), specifically designed for FPGA platforms. Combined with a new matrix compression technique, the accelerator effectively exploits unstructured sparsity in con-volutional filters to improve resource utilization and accelerate processing speed. It also incorporates a distributed partial result reduction mechanism, offering cost-effective reduction circuits. Experimental results demonstrate that, in selected configurations, the proposed accelerator achieves average processing time reductions of 38.8%, 38.7%, and 35.4% for the convolutional layers of 70% sparse ResNet18, VGG16, and EfficientNetV2-S, respectively. The results also highlight the accelerator’s excellent energy efficiency when computing highly-accurate pruned models.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".