He-P2012: Architectural heterogeneity exploration on a scalable many-core platform
Bibliographic record
Abstract
Architectural heterogeneity is a promising solution to overcome the utilization wall and provide Moore's Law-like performance scaling in future SoCs. However, heterogeneous architectures increase the size and complexity of the design space along several axes: granularity of the heterogeneous processors, coupling with the software cores, communication interfaces, etc. As a consequence, significant enhancements are required to tools and methodologies to explore the huge design space effectively. In this work, we provide three main contributions: first, we describe an extension to the STMicroelectronics P2012 platform to support tightly-coupled shared memory HW processing elements (HWPE), along with our changes to the P2012 simulation flow to integrate this extension. Second, we propose a novel methodology for the semi-automatic definition and instantiation of HWPEs from a C program based on a interface description language. Third, we explore several architectural variants on a set of benchmarks originally developed for the homogeneous version of P2012, achieving up to 123x speedup for the accelerated code region (~98% of the Amdahl limit for the whole application), thereby demonstrating the efficiency of tightly memory-coupled hardware acceleration.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".