This book is intended for researchers, teachers, and students willing to ex-
plore conceptual bridges between the fields of Automatic Control and Power
Electronics. The need to bring the two disciplines closer has been felt, for
many years, both by Power Electronics specialists and by Automatic Control
theorists, as a means of fruitful interaction between the two scientific com-
munities. There have, certainly, been many steps given in that direction in
the last decade as evidenced by the number of research articles in journals,
special sessions in conferences, and summer courses throughout the world.
標(biāo)簽：
Techniques
Control
Design
上傳時(shí)間：
2020-06-07
上傳用戶(hù)：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(shí)間：
2020-06-10
上傳用戶(hù)：shancjb
