Polyblaze: From one to many bringing the microblaze into the multicore era with Linux SMP support
Bibliographic record
Abstract
Modern computing systems increasingly consist of multiple processor cores. From cell phones to datacenters, multicore computing has become the standard. At the same time, our understanding of the performance impact resource sharing has on these platforms is limited, and therefore, prevents these systems from being fully utilized. As the capacity of FPGAs has grown, they have become a viable method for emulating architecture designs as they offer increased performance and visibility into runtime behaviour compared to simulation. With future systems trending towards asymmetric and heterogeneous systems, and thus further increasing complexity, a framework that enables research in this area is highly desirable. In this work, we present PolyBlaze: a multicore Micro- Blaze based system with Linux Symmetric Multi-Processor (SMP) support on an FPGA. Starting with a single-core, Linux supported, MicroBlaze we detail the changes to the platform, both in hardware and software, required to bring Linux SMP support to the MicroBlaze. We then outline the series of tests performed on our platform to demonstrate both its stability (e.g. more than two weeks of up time) and scalability (up to eight cores on an FPGA, with resource usage increasing linearly with the number of cores).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".