Stripes: Bit-serial deep neural network computing
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
Motivated by the variance in the numerical precision requirements of Deep Neural Networks (DNNs) [1], [2], Stripes (STR), a hardware accelerator is presented whose execution time scales almost proportionally with the length of the numerical representation used. STR relies on bit-serial compute units and on the parallelism that is naturally present within DNNs to improve performance and energy with no accuracy loss. In addition, STR provides a new degree of adaptivity enabling on-the-fly trade-offs among accuracy, performance, and energy. Experimental measurements over a set of DNNs for image classification show that STR improves performance over a state-of-the-art accelerator [3] from 1.30x to 4.51x and by 1.92x on average with no accuracy loss. STR is 57% more energy efficient than the baseline at a cost of 32% additional area. Additionally, by enabling configurable, per-layer and per-bit precision control, STR allows the user to trade accuracy for further speedup and energy efficiency.
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 | 0.000 |
| 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