Portable, Flexible, and Scalable Soft Vector Processors
Bibliographic record
Abstract
Field-programmable gate arrays (FPGAs) are increasingly used to implement embedded digital systems, however, the hardware design necessary to do so is time-consuming and tedious. The amount of hardware design can be reduced by employing a microprocessor for less-critical computation in the system. Often this microprocessor is implemented using the FPGA reprogrammable fabric as a soft processor which presently have simple architectures and moderate performance. Our goal is to scale the performance of existing soft processors hence expanding their suitability to more critical computation. To this end we propose extending soft processors with vector extensions to exploit the abundant data parallelism found in many embedded kernels. Such a soft vector processor can execute these kernels much faster than a single-core hence reducing the need for hardware implementations. We observe this improved execution speed through experimentation with vector extended soft processor architecture (VESPA) which is designed, implemented, and evaluated on real FPGA hardware. VESPA is shown to effectively scale performance up to 32 lanes, while providing substantial architectural flexibility to create a fine-grained design space. With these characteristics, and portability across FPGA devices, soft vector processors can provide exact-fit architectures which can efficiently and more easily implement data parallel workloads over custom FPGA hardware design.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".