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Among the components that can fortify computer security there is a relatively new entrant known as the memristor (contraction of memory resistor).ABSTRACT | Information security has emerged as an important system and application metric. Classical security solutions use algorithmic mechanisms that address a small subset of emerging security requirements, often at high-energy and performance overhead. Further, emerging side-channel and physical attacks can compromise classical security solutions. Hardware security solutions overcome many of these limitations with less energy and performance overhead. Nanoelectronics-based hardware security preserves these advantages while enabling conceptually new security primitives and applications. This tutorial paper shows how one can develop hardware security primitives by exploiting the unique characteristics such as complex device and system models, bidirectional operation, and nonvolatility of emerging nanoelectronic devices. This paper then explains the security capabilities of several emerging nanoelectronic devices: memristors, resistive random-access memory, contact-resistive random-access memory, phase change memories, spin torque-transfer random-access memory, orthogonal spin transfer random access memory, graphene, carbon nanotubes, silicon nanowire field-effect transistors, and nanoelectronic mechanical switches. Further, the paper describes hardware security primitives for authentica-tion, key generation, data encryption, device identification, digital forensics, tamper detection, and thwarting reverse engineering. Finally, the paper summarizes the outstanding challenges in using emerging nanoelectronic devices for security.In this paper, we refer a non-CMOS, nanoscale, emerging technology device as a nanoelectronic device. 0018-9219

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Reservoir computing (RC) is gaining traction in several signal processing domains, owing to its non-linear stateful computation, spatiotemporal encoding, and reduced training complexity over recurrent neural networks (RNNs). Previous studies have shown the effectiveness of software-based RCs for a wide spectrum of applications. A parallel body of work indicates that realizing RNN architectures using custom integrated circuits and reconfigurable hardware platforms yields significant improvements in power and latency. In this research, we propose a neuromemristive RC architecture, with doubly twisted toroidal structure, that is validated for biosignal processing applications. We exploit the device mismatch to implement the random weight distributions within the reservoir and propose mixed-signal subthreshold circuits for energy efficiency. A comprehensive analysis is performed to compare the efficiency of the neuromemristive RC architecture in both digital(reconfigurable) and subthreshold mixed-signal realizations. Both Electroencephalogram (EEG) and Electromyogram (EMG) biosignal benchmarks are used for validating the RC designs. The proposed RC architecture demonstrated an accuracy of 90 and 84% for epileptic seizure detection and EMG prosthetic finger control, respectively.

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