This book will discuss the topic of Control Systems, which is an interdisciplinary engineering
topic. Methods considered here will consist of both "Classical" control methods, and
"Modern" control methods. Also, discretely sampled systems (digital/computer systems) will
be considered in parallel with the more common analog methods. This book will not focus
on any single engineering discipline (electrical, mechanical, chemical, etc.), although readers
should have a solid foundation in the fundamentals of at least one discipline.
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wiki-Control_Systems
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2020-06-10
上傳用戶：shancjb
although state of the art in many typical machine learning tasks, deep learning
algorithmsareverycostly interms ofenergyconsumption,duetotheirlargeamount
of required computations and huge model sizes. Because of this, deep learning
applications on battery-constrained wearables have only been possible through
wireless connections with a resourceful cloud. This setup has several drawbacks.
First, there are privacy concerns. Cloud computing requires users to share their raw
data—images, video, locations, speech—with a remote system. Most users are not
willing to do this. Second, the cloud-setup requires users to be connected all the
time, which is unfeasible given current cellular coverage. Furthermore, real-time
applications require low latency connections, which cannot be guaranteed using
the current communication infrastructure. Finally, wireless connections are very
inefficient—requiringtoo much energyper transferredbit for real-time data transfer
on energy-constrained platforms.
標(biāo)簽：
Embedded_Deep_Learning
Algorithms
上傳時間：
2020-06-10
上傳用戶：shancjb
