Energy-Efficient Hardware for Deep Convolutional Neural Networks

  • Authors:
    Vivienne Sze (MIT)
    Publication ID:
    Publication Type:
    Received Date:
    Last Edit Date:
    2700.001 (Mass. Institute of Technology)
    2700.002 (Mass. Institute of Technology)
    Replay is not available


This talk will describe methods to enable energy-efficient processing of deep convolutional neural networks (CNN), which are the cornerstone of many deep-learning algorithms. While CNNs deliver record-breaking accuracy for many computer vision tasks, they require significant computation resources due to the size of the networks (e.g. hundreds of megabytes for filter weights storage and 30k-600k operations per input pixel). In this talk, we will discuss how to efficiently manage these large networks using a new CNN dataflow, called row stationary, that maximizes data reuse to minimize data movement both on- and off-chip for optimal energy-efficiency and throughput. We will present our reconfigurable accelerator named Eyeriss that supports the energy-efficient row-stationary dataflow, as well as exploits data statistics, making it 10x more energy-efficient than most mobile GPUs. The chip has been integrated into a real-time image recognition system and can be configured to efficiently support a wide range of CNNs including AlexNet and VGG-16.

Past Events

  Event Summary
10 January 2017
Energy-Efficient Hardware for Deep Convolutional Neural Networks
Tuesday, Jan. 10, 2017, 4 p.m.–5 p.m. ET
Durham, NC, United States


4819 Emperor Blvd, Suite 300 Durham, NC 27703 Voice: (919) 941-9400 Fax: (919) 941-9450

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