Atalanta: A Bit is Worth a “Thousand” Tensor Values
Why this work is in the frame
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Bibliographic record
Abstract
Atalanta is a lossless, hardware/software co-designed compression technique for the tensors of fixed-point quantized deep neural networks. Atalanta increases effective memory capacity, reduces off-die traffic, and/or helps to achieve the desired performance/energy targets while using smaller off-die memories during inference. Atalanta is architected to deliver nearly identical coding efficiency compared to Arithmetic Coding while avoiding its complexity, overhead, and bandwidth limitations. Indicatively, the Atalanta decoder and encoder units each use less than 50B of internal storage. In hardware, Atalanta is implemented as an assist over any machine learning accelerator transparently compressing/decompressing tensors just before the off-die memory controller. This work shows the performance and energy efficiency of Atalanta when implemented in a 65nm technology node. Atalanta reduces data footprint of weights and activations to 60% and 48% respectively on average over a wide set of 8-bit quantized models and complements a wide range of quantization methods. Integrated with a Tensorcore-based accelerator, Atalanta boosts the speedup and energy efficiency to 1.44× and 1.37×, respectively. Atalanta is effective at compressing the stashed activations during training for fixed-point inference.
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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 | 0.001 |
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