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

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Foreshadow

https://doi.org/10.1007/978-3-662-68073-5 neural networks can be accelerated in an energy-efficient manner. In particular, we focus on design considerations and trade-offs for mapping CNNs, Transformers, and GNNs on AI accelerators that attempt to maximize compute efficiency and minimize energy consumption by reducing the number of access to memory through efficient data reuse.

遺傳

https://doi.org/10.1007/978-3-8349-9996-2are. Thereafter, we discuss different interconnect techniques for IMC architectures proposed in the literature. Finally, different performance evaluation techniques for IMC architectures are described. We conclude the chapter with a summary and future avenues for IMC architectures for ML acceleration.

陶醉

Low- and Mixed-Precision Inference Acceleratorsd, all aiming at enabling neural network inference at the edge. In this chapter, design choices and their implications on the flexibility and energy efficiency of several accelerators supporting extremely quantized networks are reviewed.

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圓柱

In-Memory Computing for AI Accelerators: Challenges and Solutionsare. Thereafter, we discuss different interconnect techniques for IMC architectures proposed in the literature. Finally, different performance evaluation techniques for IMC architectures are described. We conclude the chapter with a summary and future avenues for IMC architectures for ML acceleration.

圓柱

生命

Cryptic

掙扎

Embedded Machine Learning for Cyber-Physical, IoT, and Edge ComputingHardware Architectur