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/10.1007/978-3-8349-9996-2date, several SRAM/ReRAM-based IMC hardware architectures to accelerate ML applications have been proposed in the literature. However, crossbar-based IMC hardware poses several design challenges. In this chapter, we first describe different machine learning algorithms adopted in the literature recen

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Meiofauna Sampling and Processing,tance for training ML models. With this comes the challenge of overall efficient deployment, in particular low-power and high-throughput implementations, under stringent memory constraints. In this context, non-volatile memory (NVM) technologies such as spin-transfer torque magnetic random access me

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The Earlier Cytological Investigations,he increasing memory intensity of most DNN workloads, main memory can dominate the system’s energy consumption and stall time. One effective way to reduce the energy consumption and increase the performance of DNN inference systems is by using approximate memory, which operates with reduced supply v

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Geschichtliche Perspektiven der Problemlage,CPUs and GPUs. Such accelerators are thus well suited for resource-constrained embedded systems. However, mapping sophisticated neural network models on these accelerators still entails significant energy and memory consumption, along with high inference time overhead. Binarized neural networks (BNN

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https://doi.org/10.1007/978-3-031-19568-6Machine learning embedded systems; Machine learning IoT; Machine learning edge computing; Smart Cyber-P

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978-3-031-19570-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl