Titlebook: Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing; Hardware Architectur Sudeep Pasricha,Muhammad Shafique Book 2024 The

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https://doi.org/10.1007/978-3-642-96347-6use of the system software for many different hardware platforms. We describe how platform-based design methodologies can be applied to neuromorphic system design. Specifically, we show that a given system software framework can be optimized to achieve performance, energy, and reliability goals of a

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Geschichtliche Perspektiven der Problemlage,e level, explore efficient corrective tuning for these devices, and integrate circuit-level optimization to counter thermal variations. As a result, the proposed . architecture possesses the desirable traits of being robust, energy-efficient, low latency, and high throughput, when executing BNN mode

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https://doi.org/10.1007/978-3-663-02695-2paradigm has been explored to improve the energy-efficiency of silicon photonic networks-on-chip (PNoCs). Silicon photonic interconnects suffer from high power dissipation because of laser sources, which generate carrier wavelengths and tuning power required for regulating photonic devices under dif

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https://doi.org/10.1007/978-3-662-68073-5alized accelerators to meet strict latency and energy constraints that are prevalent in both edge and cloud deployments. These accelerators achieve high performance through parallelism over hundreds of processing elements, and energy efficiency is achieved by reducing data movement and maximizing re

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https://doi.org/10.1007/978-3-658-38198-1e a prominent solution for many machine learning (ML) tasks, like personalized healthcare assistance. Such implementations require high energy efficiency since embedded applications usually have tight operational constraints, such as small memory and low operational power/energy. Therefore, speciali