Abstract
Binarized neural network (BNN) is one of the most promising solution for low-cost convolutional neural network acceleration. Since BNN is based on binarized bit-level operations, there exist great opportunities to reduce power-hungry data transfers and complex arithmetic operations. In this paper, we propose a content addressable memory (CAM) based BNN accelerator. By using time-domain signal processing, the huge convolution operations of BNN can be effectively replaced to the CAM search operation. In addition, thanks to fully parallel search of CAM, the parallel convolution operations for non-overlapped filtering window is enabled for high throughput data processing. To verify the effectiveness of the proposed CAM based BNN accelerator, the convolutional layer of LeNet-5 model has been implemented using 65nm CMOS technology. The implementation results show that the proposed BNN accelerator achieves 9.4% and 38.5% of area and energy savings, respectively. The parallel convolution operation of the proposed approach also shows 2.4x improved processing time.
Original language | English |
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Title of host publication | Proceedings of the 55th Annual Design Automation Conference, DAC 2018 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Print) | 9781450357005 |
DOIs | |
Publication status | Published - 2018 Jun 24 |
Event | 55th Annual Design Automation Conference, DAC 2018 - San Francisco, United States Duration: 2018 Jun 24 → 2018 Jun 29 |
Publication series
Name | Proceedings - Design Automation Conference |
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Volume | Part F137710 |
ISSN (Print) | 0738-100X |
Other
Other | 55th Annual Design Automation Conference, DAC 2018 |
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Country/Territory | United States |
City | San Francisco |
Period | 18/6/24 → 18/6/29 |
Bibliographical note
Publisher Copyright:© 2018 Association for Computing Machinery.
Keywords
- Binarized neural network
- Content addressable memory
- Timedomain signal processing
ASJC Scopus subject areas
- Computer Science Applications
- Control and Systems Engineering
- Electrical and Electronic Engineering
- Modelling and Simulation