From Pthreads to Multicore Hardware Systems in LegUp High-Level Synthesis for FPGAs
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
In the last decade, processor speeds have remained fairly stagnant, and to improve performance further, the industry started to increase the number of processor cores. The use of specialized hardware, such as field-programmable gate arrays (FPGAs), has also been on the rise. The traditional design methodology for FPGAs, however, requires hardware knowledge, which makes the platform inaccessible to software engineers. High-level synthesis (HLS) tools aim to resolve this issue by allowing software design methodologies to be used for FPGAs. However, HLS remains difficult to use for many software engineers, as there are tasks, such as system integration, which is still mostly a manual process. Consequently, creating a multicore hardware system on an FPGA is not feasible for most software engineers. To this end, we provide an HLS framework, which can automatically generate a multicore hardware system from software. We provide support for POSIX threads, which can be compiled to concurrently executing hardware cores that can be used in a processor-accelerator hybrid system, or in a hardware-only system without a processor. With this, we show that we can create multicore FPGA systems that can provide significant benefits in performance and energy-efficiency compared with hardware executing sequentially, and software executing on MIPS/ARM/x86 processors.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 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