FPRaker: Exploiting Fine-grain Sparsity to Accelerate Neural Network Training
Bibliographic record
Abstract
sis presents FPRaker, a processing element for composing training accelerators. Training manipulates floating-point data and multiply-accumulate (MAC) operations constitute the bulk of its computations. FPRaker boosts performance and energy-efficiency by skipping ineffectual computations during training. FPRaker processes the operands’ significand of each MAC as a series of signed-powers-of-two, or terms. This exposes ineffectual work that can be skipped: encoded values have few terms and some can be discarded as they would fall outside the accumulator's precision. Over 9 studied networks, FPRaker is 1.5x faster and 1.4x more energy-efficient compared to a baseline accelerator with conventional TensorCore-like tiles under iso-compute-area constraints. We demonstrate that FPRaker delivers additional benefits when training incorporates pruning, quantization and methods that use a different accumulator precision per layer. Finally, we propose a memory compression technique for exponents of floating-point values that exploits the narrow value distribution during training using base-delta compression reducing off-chip memory bandwidth.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".