A Novel Architecture for Early Detection of Negative Output Features in Deep Neural Network Accelerators
Bibliographic record
Abstract
Deep Neural Networks (DNNs) perform billions of computations in applications such as image classification, object detection, and image segmentation. In most well-known DNNs, an activation function follows a convolutional or a fully connected layer. Several popular activation functions involve setting all negative inputs to zero. In this brief, we propose a novel architecture in which the activation function is merged with the prior computational layer. Our proposed dataflow can reduce the computations needed to generate a specific output feature in convolutional and fully connected layers by reordering the computation to achieve early detection of the sign of the output feature. In addition, our computation engine supports zero computation skipping, which further accelerates the layer computation. In this brief, we build on a state-of-the-art DNN accelerator and modify it to perform early detection of negative output features. When compared to the original design, our method achieves a speedup of ×2.19 and reduces energy consumption by ×1.94. The average reduction in the number of multiply-accumulate (MAC) operations is 10.64% and the average reduction in the number of the load operations is 3.86%. These improvements are achieved while maintaining classification accuracy in two popular benchmark networks.
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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) | 0.001 | 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 | 0.001 |
| 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".