Design and Test of Adaptive Computing Fabrics for Scalable and High-Efficiency Cognitive SoC Applications
Bibliographic record
Abstract
In this paper, a new adaptive computing fabric (ACF) that achieves both real-time multi-mode/multi-rate adaptation and lower error floor for cognitive SoC applications is presented. The VLSI architecture of the ACF is experimentally shown to meet the DVB, 802.3an and 802.ad target specifications. Our design delivers a 10-14 bit error rate (BER) with a bit energyto- noise density of Eb/N0=5dB with an energy-efficiency of 0.61pJ/bit. Experiments are conducted comparing Low-Density Parity-Check (LDPC) codes error correction performance in the presence of unreliable circuits due to aggressive manufacturing defect rates and/or run-time defect rates from components enabled by SoC integration. We report on a 201.6Gbps 65nm CMOS design and Xilinx FPGA prototype, which demonstrates in hardware how real-time adaptive techniques can accelerate decoding convergence and lower the error floor. Finally, We show experimentally that our ACF design can achieve energyefficiency throughput speed-ups at scale in the range of 200x to 5000x as compared to the same algorithm running in software (optimized C program) on a single CPU core.
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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) | 0.000 | 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".