Enabling Highly Efficient Capsule Networks Processing Through Software-Hardware Co-Design
| Autor: | Donglin Zhuang, Xin Fu, Shuaiwen Leon Song, Chenhao Xie, Xingyao Zhang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
010302 applied physics
Artificial neural network business.industry Computer science Image processing 02 engineering and technology 01 natural sciences Convolutional neural network Object detection 020202 computer hardware & architecture Theoretical Computer Science Software Computational Theory and Mathematics Hardware and Architecture 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Redundancy (engineering) System on a chip Routing (electronic design automation) business Computer hardware |
| Zdroj: | IEEE Transactions on Computers. 70:495-510 |
| ISSN: | 2326-3814 0018-9340 |
| Popis: | As the demand for the image processing increases, the image features become increasingly complicated. Although the Convolutional Neural Network (CNN) have been widely adopted for the imaging processing tasks, it has been found easily misled due to the massive usage of pooling operations. A novel neural network structure called Capsule Networks (CapsNet) is proposed to address the CNN challenge and essentially enhance the learning ability for the image segmentation and object detection. Since the CapsNet contains the high volume of the matrix execution, it has been generally accelerated on modern GPU platforms with the highly optimized deep-learning library. However, the routing procedure of CapsNet introduces the special program and execution features,including massive unshareable intermediate variables and intensive synchronizations, causing inefficient CapsNet execution on modern GPU. To address these challenges, we propose the software-hardware co-designed optimizations, SH-CapsNet, which includes the software-level optimizations named S-CapsNet and a hybrid computing architecture design named PIM-CapsNet . In software-level, S-CapsNet reduces the computation and memory accesses by exploiting the computational redundancy and data similarity of the routing procedure. In hardware-level, the PIM-CapsNet leverages the processing-in-memory capability of today's 3D stacked memory to conduct the off-chip in-memory acceleration solution for the routing procedure, while pipelining with the GPU's on-chip computing capability for accelerating CNN types of layers in CapsNet. Evaluation results demonstrate that either our software or hardware optimizations can significantly improve the CapsNet execution efficiency. Together, our co-design can achieve greatly improvement on both performance ( $3.41\times$ 3 . 41 × ) and energy savings (68.72 percent) for CapsNet inference, with negligible accuracy loss. |
| Databáze: | OpenAIRE |
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