Towards Reliability Assessment of Systolic Arrays against Stuck-at Faults
Bibliographic record
Abstract
Neural Networks are ubiquitously used in safety-critical applications such as autonomous vehicles and medical diagnostics. The increasing complexity and compute-intensiveness of deep neural networks (DNN) have motivated the need for DNN accelerators like Google’s Tensor Processing Unit (TPU) to accelerate convolution and matrix multiplication operations. At its core, a TPU consists of a 2-Dimensional array of Multiply and Accumulation Units, called a systolic array, which is susceptible to permanent (e.g., stuck-at faults in the data path) and transient hardware faults (e.g., radiation-induced). We propose an RTL-level fault injection (FI) framework for systolic arrays. Using this framework, we characterize the software effect of errors (called Fault Patterns) induced by stuck-at faults within the multiply and accumulation units of the systolic array. We further analyze the effect of different data flows mapping schemes (output and weight stationery), operation types (convolution and matrix multiplication), and operation configurations (e.g., input size, convolution kernel size). Through the FI experiments, we categorized the fault patterns for stuck-at faults into well-defined classes based on their spatial patterns.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".