Home » Low-Power Computer Vision
Product Image

Low-Power Computer Vision

€95.99
Quantity:
In stock with our UK publisher. 14-28 days
Delivery/Collection within 10-20 working days
14 days return policy Shipping & Delivery
Accuracy Drop
Architecture Search
Artificial Intelligence
Category=UT
Category=UYQN
Category=UYQV
CNN Model
Computer Vision
Convolution Layers
Convolutional Layer
CV
Dart
Deep NN Model
DNN
efficient machine learning deployment
embedded vision systems
energy-efficient algorithms
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Execution Time
Hardware
Hardware Accelerators
heterogeneous computing
Image Recognition Systems
Initial Learning Rate
Input Feature Maps
Low Power
model compression techniques
Multiply Accumulate Operations
Neural Network
Neural Network Quantization
Neural Networks
NN
NN Architecture
Object Detection
quantisation methods
Quantize Weights
Search Space
Seoul National University
SoC optimisation
Software
Top-1 Accuracy
Tsinghua University

Product details

  • ISBN 9780367744700
  • Weight: 870g
  • Dimensions: 156 x 234mm
  • Publication Date: 23 Feb 2022
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
Secure checkout Fast Shipping Easy returns
Description
Energy efficiency is critical for running computer vision on battery-powered systems, such as mobile phones or UAVs (unmanned aerial vehicles, or drones). This book collects the methods that have won the annual IEEE Low-Power Computer Vision Challenges since 2015. The winners share their solutions and provide insight on how to improve the efficiency of machine learning systems.
About

George K. Thiruvathukal is a professor of Computer Science at Loyola University Chicago, Illinois, USA. He is also a visiting faculty at Argonne National Laboratory. His research areas include high performance and distributed computing, software
engineering, and programming languages.

Yung-Hsiang Lu is a professor of Electrical and Computer Engineering at Purdue University, Indiana, USA. He is the first director of Purdue’s John Martinson Engineering Entrepreneurial Center. He is a fellow of the IEEE and distinguished scientist of the ACM. His research interests include computer vision, mobile systems, and cloud computing.

Jaeyoun Kim is a technical program manager at Google, California, USA. He leads AI research projects, including MobileNets and TensorFlow Model Garden, to build state-of-the-art machine learning models and modeling libraries for computer vision and natural language processing.

Yiran Chen is a professor of Electrical and Computer Engineering at Duke University, North Carolina, USA. He is a fellow of the ACM and the IEEE. His research areas include new memory and storage systems, machine learning and neuromorphic
computing, and mobile computing systems.

Bo Chen is the Director of AutoML at DJI, Guangdong, China. Before joining DJI, he was a researcher at Google, California, USA. His research interests are the optimization of neural network software and hardware as well as landing AI technology in products with stringent resource constraints.