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The Hardware and Algorithm Co-Design for Energy-Efficient DNN Processor on Edge/Mobile Devices
- Source :
- IEEE Transactions on Circuits and Systems I: Regular Papers. 67:3458-3470
- Publication Year :
- 2020
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2020.
-
Abstract
- Deep neural network (DNN) has been widely studied due to its high performance and usability for various applications such as image classification, detection, segmentation, translation, and action recognition. Thanks to the universal applications and high performance of DNN algorithm, DNN is adopted for various AI platforms, including edge/mobile devices as well as cloud servers. However, high-performance DNN requires a large amount of computation and memory access, making it challenging to implement DNN operation on edge/mobile. There have been several ways to solve these problems, including algorithms as well as hardware for DNN. Algorithms that help accelerate DNN in hardware enable much more efficient operation of high-performance AI. This article aims to provide an overview of the recent hardware and algorithm co-design schemes enabling efficient processing of DNNs. Specifically, it will provide algorithm optimization methods for DNN structure, neurons, synapses, and data types. This paper also introduces optimization methods for hardware architectures, PE array, data-path control, and microarchitecture of PE. And we will also show examples of DNN algorithm and hardware co-designed ASICs.
- Subjects :
- 010302 applied physics
Contextual image classification
Artificial neural network
business.industry
Computer science
Usability
02 engineering and technology
01 natural sciences
Data type
020202 computer hardware & architecture
Microarchitecture
0103 physical sciences
0202 electrical engineering, electronic engineering, information engineering
Enhanced Data Rates for GSM Evolution
Electrical and Electronic Engineering
business
Mobile device
Algorithm
Computer hardware
Efficient energy use
Subjects
Details
- ISSN :
- 15580806 and 15498328
- Volume :
- 67
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Circuits and Systems I: Regular Papers
- Accession number :
- edsair.doi...........d2c3535c2bf8ef447d2a4e1c2f509ee2
- Full Text :
- https://doi.org/10.1109/tcsi.2020.3021397