PASoC: A Predictable Accelerator Rich SoC for Safety-Critical Systems
Bibliographic record
Abstract
This thesis presents a model of a Predictable Accelerator-rich System-on-Chip (PASoC) \nfor safety-critical systems, which guarantees timing predictability of a memory access in \nthe system. Earlier adoption of accelerator-rich SoCs was for general-purpose comput ing and thus timing predictability of such systems was not well explored, despite being \nused in safety-critical systems. This thesis takes initial steps in exploring the predictabil ity of ASoCs by combining CPU clusters with one or more hardware accelerators. The \nPASoC allows the integration of multiple coherent agents to interact with each other over \na shared memory bus and a shared LLC. These agents can be a cluster of cache-coherent \nhomogeneous cores, and fully or one-way coherent hardware accelerators. PASoC ensures \nthe predictability of a memory request through some modifications in hardware architecture \nand cache coherence protocols. PASoC supports predictable cache coherence within the \ncluster of cores and across agents. The former uses linear cache coherence, and the latter \nuses a modified version of predictable Modified Shared Invalid (MSI) cache coherence pro tocol. PASoC analyzes the per-request worst-case latency of a memory request from any \nof the agents and evaluates the design on the gem5 simulator. Finally, this work presents \nsome observations based on the analysis that can help in future designs of PASoCs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".