Deep neural network acceleration framework under hardware uncertainty
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Deep Neural Networks (DNNs) are known as effective model to perform cognitive tasks. However, DNNs are computationally expensive in both train and inference modes as they require the precision of floating point operations. Although, several prior work proposed approximate hardware to accelerate DNNs inference, they have not considered the impact of training on accuracy. In this paper, we propose a general framework called FramNN, which adjusts DNN training model to make it appropriate for underlying hardware. To accelerate training FramNN applies adaptive approximation which dynamically changes the level of hardware approximation depending on the DNN error rate. We test the efficiency of the proposed design over six popular DNN applications. Our evaluation shows that in inference, our design can achieve 1.9× energy efficiency improvement and 1.7× speedup while ensuring less than 1% quality loss. Similarly, in training mode FramNN can achieve 5.0× energy-delay product improvement as compared to baseline AMD GPU.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it