An energy-efficient deep convolutional neural networks coprocessor for multi-object detection. (April 2020)

• Record Type: Journal Article
• Authors: Wu, Yuancong; Wang, J.J.; Qian, Kun; Liu, Yanchen; Guo, Rui; Hu, S.G.; Yu, Q.; Chen, T.P.; Liu, Y.; Rong, Limei
• Is Part Of: Microelectronics journal. Volume 98(2020)
• Publication Date: 2020-04
• Subjects: Energy-efficient -- Deep convolution neural networks -- Coprocessor -- Deep learning -- Multi-object detection -- Hardware accelerators -- Mobile devices; Microelectronics -- Periodicals; Microélectronique -- Périodiques; Microelectronics; Electronic journals; Journals - contents and abstracts; Periodicals; 621.3805
• DOI: 10.1016/j.mejo.2020.104737
• Languages: English
• ISSNs: 0959-8324

Abstract

Abstract: This paper proposed an energy-efficient deep convolution neural networks coprocessor (DCNNs-CP) architecture for multi-object detection applications based on deep learning algorithms. The DCNNs-CP can support both convolutional layers and fully connected layers to accelerate various mobile deep learning algorithms. It also supports maximum and mean pooling operations through a separate pooling module structure. Besides, a reconfigurable activation function module supporting four nonlinear functions is also realized in this coprocessor. The DCNNs-CP chip was implemented in 55 nm CMOS process technology and occupied the 4 mm 2 die area. The DCNNs-CP supports 8-bit and 16-bit fixed-point data precision and achieves a peak performance of 3.4 Tops / W at 1.2 V supply voltage and a maximum frequency of 500 MHz, represent 2.13x improvements over reported hardware accelerators. Besides, the chip achieves 0.85 Tops / W · mm 2 energy efficiency per area and 34.0 Tops / W · MB energy efficiency per memory (on-chip memory), making it suitable to be integrated with the mobile devices.