Bibliographic record
Abstract
I will be reviewing our efforts in identifying value properties of Deep Learning models that hardware accelerators can use to improve execution time performance and energy efficiency. Our goal it to not sacrifice accuracy and to not require any changes to the model. I will be presenting our accelerator family which includes designs that exploit these properties. Our accelerators exploit ineffectual activations and weights, their variable precision requirements, or even their value content at the bit level. We have demonstrated performance benefits of 50% to up 27× over a highly optimized execution engine for neural networks. Further, our accelerators also enable on-the-fly trading off accuracy for further performance and energy efficiency improvements. I will emphasize our latest designs, Diffy, Tactical and Laconic. Tactical targets sparse models whereas Laconic achieves the highest performance when configured for embedded class devices. Diffy opens up new opportunities for deep learning models as it favors models where the values are spatially correlated. Computational Imaging models naturally exhibit such values. All our designs work with out-of-the-box networks and require no modifications or retraining. They deliver immediate benefits but also provide an incentive for further innovation in model design such as targeting a reduction in precision.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".