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Overcoming Computational Errors in Sensing Platforms Through Embedded Machine-Learning Kernels

Overcoming Computational Errors in Sensing Platforms Through Embedded Machine-Learning Kernels

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Overcoming Computational Errors in Sensing Platforms Through Embedded Machine-Learning Kernels

 We present an approach for overcoming computational errors at run time that originate from static hardware faults in digital processors. The approach is based on embedded machine learning stages that learn and model the statistics of the computational outputs in the presence of errors, resulting in an error-aware model for embedded analysis. We demonstrate, in hardware, two systems for analyzing sensor data: 1) an EEG-based seizure detector and 2) an ECG-based cardiac arrhythmia detector. The systems use a small kernel of fault-free hardware (constituting <7.0% and <31% of the total areas respectively) to construct and apply the error-aware model. The systems construct their own error-aware models with minimal overhead through the use of an embedded activelearning framework. Via an ?eld-programmable gate array implementation for hardware experiments, stuck-at faults are injected at controllable rates within synthesized gate level netlists to permit characterization. The seizure detector demonstrates restored performance despite faults on 0.018% of the circuit odes [causing bit error rates (BERs) up to 45%], and the arrhythmia detector demonstrates restored performance despite faults on 2.7% of the circuit nodes (causing BERs up to 50%).


 


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  • Model: PROJ5302
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This product was added to our catalog on Friday 16 September, 2016.

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