Paper Info
Title
14.6 A 1.42TOPS/W deep convolutional neural network recognition processor for intelligent IoE systems.
Abstract
Transmitting massive amounts of image and audio data acquired by Internet-of-Everything (IoE) devices to data center servers for intelligent recognition processes is impractical for energy reasons, requiring in-situ processing of such data. However, algorithms accelerated by previous recognition processors [1, 2] are limited to specific applications, therefore, each IoE device may require an application-specific accelerator. On the other hand, deep convolutional neural networks (CNNs) [3] are a promising machine-learning approach, showing state-of-the-art recognition accuracy in a wide variety of applications, including both image and audio recognition. This makes CNNs a suitable candidate for a universal recognition platform for IoE devices, as described in Fig. 14.6.1. Due to the computational complexity and significant memory requirements of CNNs, a microcontroller unit (MCU) typically used for IoE devices is incapable of producing a meaningful recognition result in an energy-efficient way. Hence, the implementation of an energy-efficient CNN processor is desired to realize intelligent IoE systems.
Year
Venue
Field
2016
ISSCC
Kernel (linear algebra),System on a chip,Computer science,Convolutional neural network,Efficient energy use,Server,Microcontroller,Computer hardware,Data center,Computational complexity theory
DocType
Citations 
PageRank 
Conference
25
1.36
References 
Authors
6
6
Name
Order
Citations
PageRank
Jaehyeong Sim1527.63
Junseok Park221426.80
Minhye Kim3261.72
Dongmyung Bae4261.72
Yeongjae Choi5455.78
Lee-Sup Kim6271.78