The Challenges of Next Generation Automotive Benchmarks
Bibliographic record
Abstract
<div class="htmlview paragraph">More than ever, microcontroller performance in cars has a direct impact on the driving experience, on compliance with improved safety, ever-stricter emissions regulations, and on fuel economy. The simple microcontrollers formerly used in automobiles are now being replaced by powerful number-crunchers whose performance can no longer be measured in MIPS. Instead, their effectiveness is based on a coherent partitioning between analog and digital, hardware and software, tools and methodology. To make an informed choice among the available devices, what the designer needs are benchmarks that are specific to automotive applications, and which provide a realistic representation of how the device will perform in the automotive environment.</div> <div class="htmlview paragraph">This presentation will explore the role of new benchmarks in the development of complex automotive applications. The challenges of developing such benchmarks will be provided, the hardware required for their implementation, and the process by which a new suite of benchmarks that addresses these concerns is being implemented by EEMBC. The goal of this suite is to take every factor into consideration in creating an accurate measurement of a system's true performance and thus to develop a benchmark recognized by the worldwide automotive vendors for its efficacy in measuring the performance of every component in automotive applications including microcontroller (μC) cores, buses, peripherals, memory, compilers, profilers, operating systems, software (SW) drivers, and auto code generators. This new benchmark is being targeted to be a tool to define performance requirements between the members of the automotive chain: OEMs, Tierls, silicon suppliers, and tool vendors.</div>
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".