This paper explores an energy-efficient resistive random access memory (RRAM) crossbar array framework for predicting epileptic seizures using the CHB-MIT electroencephalogram (EEG) dataset. RRAMs have significant potential for in-memory computing, offering a promising solution to overcome the limitations of the traditional Von Neumann architecture. By integrating a domain-specific feature extraction approach and evaluating the optimal RRAM hardware parameters using the NeuroSim+ benchmarking platform, we assess the performance of RRAM crossbars for predicting epileptic seizures. Our proposed workflow achieves accuracy levels above 80% despite the EEG data being quantized to 1-bit, highlighting the robustness and efficiency of our approach for epileptic seizure prediction
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An Integrated TRNG-PUF Architecture based on Photovoltaic Solar Cells
The objective of the article is to present an integrated True Random Number Generator (TRNG) and Physically Unclonable Function (PUF) architecture using Photovoltaic solar cells. We illustrate that the Photovoltaic (PV) solar cell sensor response can be engineered into dynamic (TRNG) and static responses (PUF). The proposed prototype uses the iterative Von Neumann post-processing scheme to produce random bits with 34% better throughput compared to a single Von Neumann operation. The random bit quality was checked by statistical test suites from the National Institute of Science and Technology (NIST) and achieves an average p-value of 0.45 at all variations in light intensity. The PUF response achieves 92.13% reliability and 50.91% uniformity. The integrated TRNG-PUF architecture is beneficial for resource-constrained Cyber-Physical System (CPS).
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- Award ID(s):
- 1738662
- PAR ID:
- 10208168
- Date Published:
- Journal Name:
- IEEE Consumer Electronics Magazine
- ISSN:
- 2162-2248
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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