Test, Reliability and Functional Safety Trends for Automotive System-on-Chip
Author:
Affiliation:
1. Politecnico di Torino, IT
2. STMicroelectronics, IT
3. NVIDIA, US
4. Infineon Technologies, DE
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9810327/9810358/09810388.pdf?arnumber=9810388
Reference12 articles.
1. A Reliability Analysis of a Deep Neural Network
2. Statistical fault injection: Quantified error and confidence
3. An experiment of burn-in time reduction based on parametric test analysis
4. Exploiting Active Learning for Microcontroller Performance Prediction
5. Effective Screening of Automotive SoCs by Combining Burn-In and System Level Test
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1. A Multilabel Active Learning Framework for Microcontroller Performance Screening;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2023-10
2. Evaluation and Mitigation of Faults Affecting Swin Transformers;2023 IEEE 29th International Symposium on On-Line Testing and Robust System Design (IOLTS);2023-07-03
3. SCI-FI: a Smart, aCcurate and unIntrusive Fault-Injector for Deep Neural Networks;2023 IEEE European Test Symposium (ETS);2023-05-22
4. Special Session: Approximation and Fault Resiliency of DNN Accelerators;2023 IEEE 41st VLSI Test Symposium (VTS);2023-04-24
5. A Toolchain to Quantify Burn-In Stress Effectiveness on Large Automotive System-on-Chips;IEEE Access;2023
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