Polyblaze: From one to many bringing the microblaze into the multicore era with Linux SMP support
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it