Tackling Resource Utilization In Deep Neural Network Accelerators
Bibliographic record
Abstract
Good resource utilization plays an important role in maximizing the performance of DNN accelerators. One method to maximize resource utilization is to divide accelerator resources into multiple sub-accelerators. However, there are a couple of design considerations that are essential to the production of efficient multi-accelerator systems. The number of sub-accelerators to use and the distribution of resources are among a few considerations that are important to the design of highly efficient multi- accelerator systems. We present DataflowBay, a framework that helps guide the design of multi-accelerator systems. DataflowBay implements a scheduler that extends the state-of-the-art to map DNN layers on multi-accelerators systems with an average energy-delay product improvement of ∼11.6%. DataflowBay also implements a Bayesian optimization module to automate the fine-grained mapping of DNN layers onto the sub-accelerators and a stochastic greedy search algorithm to decide what hardware resource distribution will lead to the best performance for each sub-accelerator.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".