A Multicycle Pipelined GCM-Based AUTOSAR Communication ASIP
Bibliographic record
Abstract
In this paper, we explore two pipeline techniques to enhance performance of communication operations in AUTomotive Open System Architecture (AUTOSAR)-based automotive electronic control units (ECUs), and securing these communication operations using an enhanced two-layer process based on the highly secure Galois/Counter Mode of Operation (GCM) algorithm. Our work is based on extending a previous work that implemented three versions of AUTOSAR communication (COM) application-specific instruction set processor (ASIP). We made two pipelined architectures on top of COM ASIP V3 (i.e. COM ASIP V4 and COM ASIP V5). These new versions of COM ASIP are able to handle all operations (i.e. transmitting signals/long signals, receiving signals/long signals, calculating hash, or verifying hash for protocol data units (PDUs) containing these signals using GCM) of our previously implemented COM ASIPs in a pipelined fashion. The experimental results show that COM ASIP V4 has a speedup of 2.25x to 2.39x over COM ASIP V3, and COM ASIP V5 has a speedup of 3.27x to 5.5x over COM ASIP V3. They also show that throughput of these new versions of COM ASIP is much more, 100x to 338x, than throughput required by Controller Area Network Flexible Data Rate (CAN FD) and FlexRay communication buses.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".