Application-specific signatures for transactional memory in soft processors
Why this work is in the frame
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Bibliographic record
Abstract
As reconfigurable computing hardware and in particular FPGA-based systems-on-chip comprise an increasing number of processor and accelerator cores, supporting sharing and synchronization in a way that is scalable and easy to program becomes a challenge. Transactional Memory (TM) is a potential solution to this problem, and an FPGA-based system provides the opportunity to support TM in hardware (HTM). Although there are many proposed approaches to HTM support for ASICs, these do not necessarily map well to FPGAs. In particular in this work we demonstrate that while signature -based conflict detection schemes (essentially bit-vectors) should intuitively be a good match to the bit parallelism of FPGAs, previous approaches result in unacceptable multicycle stalls, operating frequencies, or false-conflict rates. Capitalizing on the reconfigurable nature of FPGA-based systems, we propose an application-specific signature mechanism for HTM conflict detection. Our evaluation uses real and projected FPGA-based soft multiprocessor systems that support HTM and implement threaded, shared-memory network packet processing applications. We find that our application-specific approach: (i) maintains a reasonable operating frequency of 125 MHz, (ii) achieves a 9% to 71% increase in packet throughput relative to signatures with bit selection on a 2-thread architecture, and (iii) allows our HTM to achieve 6%, 54%, and 57% increases in packet throughput on an 8-thread architecture versus a baseline lock-based synchronization for three of four packet processing applications studied, due to reduced false synchronization.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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