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

CAP

書目名稱Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing影響因子(影響力)




書目名稱Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing影響因子(影響力)學(xué)科排名




書目名稱Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing網(wǎng)絡(luò)公開度




書目名稱Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing被引頻次




書目名稱Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing被引頻次學(xué)科排名




書目名稱Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing年度引用




書目名稱Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing年度引用學(xué)科排名




書目名稱Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing讀者反饋




書目名稱Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing讀者反饋學(xué)科排名

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.

filicide

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