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Found 6 papers, 0 papers with code
Enhancing Functional Safety in Automotive AMS Circuits through Unsupervised Machine Learning
Our approach encompasses a systematic analysis of anomaly abstraction at multiple levels pertaining to the automotive domain, from hardware- to block-level, where anomalies are injected to create diverse fault scenarios.
Machine Learning-enhanced Efficient Spectroscopic Ellipsometry Modeling
The proposed approach has tremendous implications of faster data acquisition, reduced hardware complexity and easier integration of spectroscopic ellipsometry for in situ monitoring of film thickness deposition.
Exploring Fault-Energy Trade-offs in Approximate DNN Hardware Accelerators
Our quantitative analysis shows that the permanent faults exacerbate the accuracy loss in AxDNNs when compared to the accurate DNN accelerators.
High-level Modeling of Manufacturing Faults in Deep Neural Network Accelerators
The advent of data-driven real-time applications requires the implementation of Deep Neural Networks (DNNs) on Machine Learning accelerators.
Hardware Trojan Detection Using Controlled Circuit Aging
Combining IC aging with over-clocking produces a pattern of bit errors at the IC output by the induced timing violations.
Analyzing and Mitigating the Impact of Permanent Faults on a Systolic Array Based Neural Network Accelerator
Due to their growing popularity and computational cost, deep neural networks (DNNs) are being targeted for hardware acceleration.
