A highly uniform porous film of MnO was deposited on carbon fiber by anodic electrodeposition for the fabrication of high-performance electrodes in wearable supercapacitors (SCs) application. The effects of potentiostatic and galvanostatic electrodeposition and the deposition time were investigated. The morphology, crystalline structure, and chemical composition of the obtained fiber-shaped samples were analyzed by field-emission scanning electron microscopy (FESEM), X-ray diffraction (XRD), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS). The charge storage performance of the carbon fibers@MnO composite electrode coupled to a gel-like polymeric electrolyte was investigated by cyclic voltammetry and galvanostatic charge-discharge measurements. The specific capacitance of the optimized carbon fiber@MnO composite electrodes could reach up to 62 F g corresponding to 23 mF cm in PVA/NaCl gel-polymer electrolyte, i.e., the highest capacitance value ever reported for fiber-shaped SCs. Finally, the stability and the flexibility of the device were studied, and the results indicate exceptional capacitance retention and superior stability of the device subjected to bending even at high angles up to 150°.

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Neural Architecture Search (NAS) algorithms aim at finding efficient Deep Neural Network (DNN) architectures for a given application under given system constraints. DNNs are computationallycomplex as well as vulnerable to adversarial attacks. In order to address multiple design objectives, we propose RoHNAS, a novel NAS framework that jointly optimizes for adversarial-robustness and hardwareefficiency of DNNs executed on specialized hardware accelerators. Besides the traditional convolutional DNNs, RoHNAS additionally accounts for complex types of DNNs such as Capsule Networks. For reducing the exploration time, RoHNAS analyzes and selects appropriate values of adversarial perturbation for each dataset to employ in the NAS flow. Extensive evaluations on multi -Graphics Processing Unit (GPU) -High Performance Computing (HPC) nodes provide a set of Pareto-optimal solutions, leveraging the tradeoff between the above-discussed design objectives. For example, a Pareto-optimal DNN for the CIFAR-10 dataset exhibits 86.07% accuracy, while having an energy of 38.63 mJ, a memory footprint of 11.85 MiB, and a latency of 4.47 ms.

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