A Data-driven Modeling Approach to Stochastic Computation for Low-energy Biomedical Devices
- Kyong-Ho Lee ,
- Kuk Jang ,
- Shuayb Zarar ,
- Ali Shoeb ,
- Naveen Verma
IEEE/ACM Design Automation Conference (DAC) |
Published by IEEE - Institute of Electrical and Electronics Engineers
Design Automation Conference (DAC)
Data-driven modeling is a powerful way to handle errors in hardware by taking advantage of machine learning algorithms. We propose a error-aware models that handle high rate of errors in arrhythmia and seizure detection applications with nearly no extra cost in hardware.
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