Titlebook: Design and Architecture for Signal and Image Processing; 16th International W Miguel Chavarrías,Alfonso Rodríguez Conference proceedings 20

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Formal Verification of Neural Networks? characteristics when different biological components share similar spectrum. In a situation of in-vivo brain tumor location during a surgical operation, especially when applying machine learning techniques, relying solely on the spectral information of each sample may not be enough to provide a cor

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Formal Methods: Foundations and Applicationstantial impact on the overall performance of the architecture and its energy efficiency. Similarly, access to the different memories of the system, with potentially high data sharing, is a critical point. To overcome these problems, in this paper, we propose a new interconnect network, called AINoC,

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Formal Methods: Foundations and Applications/Ultra HD (. pixels @ 30 frames per second) video stream in a?4 pixel per clock (ppc) format and a?64-pixel disparity range. The baseline SGM implementation had to be modified to process pixels in the 4ppc format and meet the timing constrains, however, our version provides results comparable to the

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Cecilia Manzino,Gonzalo de Latorreces of unexpected program behavior impacting security, which can then be fixed easier by directed developer effort. When targeting IoT devices, fuzzing faces the problem that the small IoT processors often lack the observability required for fuzzing, e.g., a high-performance trace port, while softwa

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https://doi.org/10.1007/978-3-319-70848-5izing AMC is crucial to achieving the latency, throughput, and energy levels expected by the final user. State-of-the-art solutions to the AMC problem are based on Deep Learning methods (e.g., Deep Neural Networks - DNNs). However, these methods require heavy processing and high energy consumption u

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