A data-driven energy efficient and flexible compute fabric architecture: For adaptive computing applied to ULSI of FFT
Bibliographic record
Abstract
In this work, we investigate architectures that can provide the benefits of dedicated hardware implementations and the flexibility of software defined environments. We call this new approach a data defined environment, in which hardware and software scales together based on workload variability to provide state-of-the-art hardware energy-efficiency. An integrated architecture for rapidly implementing efficient large-scale Digital Signal Processing (DSP) functions is presented. The target DSP functions are represented by an application space with one or more dimensions and several ensembles of Adaptive Computing Fabrics (ACF). It is shown that the proposed fabric allows achieving deterministic performance exceeding 245GOPs/mW for data workload characterized by high dynamic variability such as FFT of various size including 64, 128, 512, 1,024, 2,048, 4,098, 8,192, 262,144 and 746,496. Experimental results show improvements on the order of 1000× in power efficiency when compared to published alternatives for several applications, including H.265 High-Efficiency Video Coding (HEVC), Bluetooth, LTE, xDSL/DVB, WLAN and mm-Waves Wireless Personal Networks (WPAN).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".