Diffy: a Déjà vu-Free Differential Deep Neural Network Accelerator
Bibliographic record
Abstract
We show that Deep Convolutional Neural Network (CNN) implementations of computational imaging tasks exhibit spatially correlated values. We exploit this correlation to reduce the amount of computation, communication, and storage needed to execute such CNNs by introducing Diffy, a hardware accelerator that performs Differential Convolution. Diffy stores, communicates, and processes the bulk of the activation values as deltas. Experiments show that, over five state-of-the-art CNN models and for HD resolution inputs, Diffy boosts the average performance by 7.1× over a baseline value-agnostic accelerator [1] and by 1.41× over a state-of-the-art accelerator that processes only the effectual content of the raw activation values [2]. Further, Diffy is respectively 1.83× and 1.36× more energy efficient when considering only the on-chip energy. However, Diffy requires 55% less on-chip storage and 2.5× less off-chip bandwidth compared to storing the raw values using profiled per-layer precisions [3]. Compared to using dynamic per group precisions [4], Diffy requires 32% less storage and 1.43× less off-chip memory bandwidth. More importantly, Diffy provides the performance necessary to achieve real-time processing of HD resolution images with practical configurations. Finally, Diffy is robust and can serve as a general CNN accelerator as it improves performance even for image classification 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 | 0.001 |
| 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 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".