Adaptive real-time DSP acceleration for SoC applications
Bibliographic record
Abstract
This paper investigates VLSI architectures for digital processing (DSP) functions amenable to low energy operation with scalable performance for H.265 high efficiency video coding (HEVC) applications. First, we describe and experimentally evaluate a novel adaptive computing fabric. Second, we propose an energy-efficient method to scale the performance of the fabric for large images or for meeting stringent real-time computation requirements. A series of tradeoffs for exploiting efficiently the application space for general purpose DSP acceleration are proposed. We experimentally show how the proposed computing fabric is reusable for Filters, FFT and DCT acceleration with a scalable throughput. We report on the design and implementation of the fabric on a Xilinx FPGA device and show how regulated-parallelism augmented with in-memory processing techniques impact performance and power efficiency. The FPGA prototype demonstrates a sustained throughput exceeding 10Gbps irrespective of the kernel and image size for H.265 HEVC applications.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".