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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".