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Hardware-Aware Probabilistic Machine Learning Models
This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models can be used to evaluate the impact that a specific device configuration may have on resource consumption and performance of the machine learning task, with the overarching goal of balancing the two optimally. The book first motivates extreme-edge computing …
- Edition
- -
- ISBN/ISSN
- 978-3-030-74042-9
- Collation
- xii, 163p.:Ill
- Series Title
- -
- Call Number
- 006.31 Ola h
Analog-and-Algorithm-Assisted Ultra-low Power Biosignal Acquisition Systems
This book discusses the design and implementation aspects of ultra-low power biosignal acquisition platforms that exploit analog-assisted and algorithmic approaches for power savings.The authors describe an approach referred to as “analog-and-algorithm-assisted” signal processing.This enables significant power consumption reductions by implementing low power biosignal acquisition systems, l…
- Edition
- 1
- ISBN/ISSN
- 978-3-030-05870-8
- Collation
- XXIII, 114p.:Ill
- Series Title
- Analog Circuits and Signal Processing
- Call Number
- 621.382 2 Pam a
Embedded Deep Learning
This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic design approaches on the application-, algorithmic-, computer architecture-, and circuit-level that will help in achieving the goal of reducing the computational cost of deep learning algorithms. The impact of these techniques is displayed in four silicon prototy…
- Edition
- 1
- ISBN/ISSN
- 978-3-319-99223-5
- Collation
- XVI, 206p.:Ill
- Series Title
- -
- Call Number
- 370.152 3 Moo e
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