Bit-Tactical
Bibliographic record
Abstract
Weight and activation sparsity can be leveraged in hardware to boost the performance and energy efficiency of Deep Neural Networks during inference. Fully capitalizing on sparsity requires re-scheduling and mapping the execution stream to deliver non-zero weight/activation pairs to multiplier units for maximal utilization and reuse. However, permitting arbitrary value re-scheduling in memory space and in time places a considerable burden on hardware to perform dynamic at-runtime routing and matching of values, and incurs significant energy inefficiencies. Bit-Tactical (TCL) is a neural network accelerator where the responsibility for exploiting weight sparsity is shared between a novel static scheduling middleware, and a co-designed hardware front-end with a lightweight sparse shuffling network comprising two (2- to 8-input) multiplexers per activation input. We empirically motivate two back-end designs chosen to target bit-sparsity in activations, rather than value-sparsity, with two benefits: a) we avoid handling the dynamically sparse whole-value activation stream, and b) we uncover more ineffectual work. TCL outperforms other state-of-the-art accelerators that target sparsity for weights and activations, the dynamic precision requirements of activations, or their bit-level sparsity for a variety of neural networks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".