Deep Learning on Edge Computing Devices: Design Challenges of
Algorithm and Architecture focuses on hardware architecture and
embedded deep learning, including neural networks. The title helps
researchers maximize the performance of Edge-deep learning models
for mobile computing and other applications by presenting neural
network algorithms and hardware design optimization approaches for
Edge-deep learning. Applications are introduced in each section,
and a comprehensive example, smart surveillance cameras, is
presented at the end of the book, integrating innovation in both
algorithm and hardware architecture. Structured into three parts,
the book covers core concepts, theories and algorithms and
architecture optimization. This book provides a solution for
researchers looking to maximize the performance of deep learning
models on Edge-computing devices through algorithm-hardware
co-design.
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