Email: eyeriss at mit dot edu
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We will be giving a two day short course on “Designing Efficient Deep Learning Systems” in Mountain View, California on March 26-27, 2018. To find out more, please visit MIT Professional Education.
We will be giving an updated version of our tutorial at MICRO-50.
Updated slides posted here from the CICS/MTL tutorial.
New paper on "Efficient Processing of Deep Neural Networks: A Tutorial and Survey" available on arXiv. [ LINK ]
DNN Energy Estimation Website available online. [ LINK ]
We will be giving an updated version of our tutorial at ISCA 2017.
New paper on “Hardware for Machine Learning: Challenges and Opportunities” will be presented at IEEE CICC 2017. Available on arXiv. [ LINK ]
New paper on “Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning” will be presented at CVPR 2017. Available on arXiv. [ LINK ]
DNN Tutorial Slides now available online.
Full day tutorial held at MICRO-49
Deep neural networks (DNNs) are currently widely used for many AI applications including computer vision, speech recognition, robotics, etc. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Accordingly, designing efficient hardware architectures for deep neural networks is an important step towards enabling the wide deployment of DNNs in AI systems.
In this tutorial, we will provide an overview of DNNs, discuss the tradeoffs of the various architectures that support DNNs including CPU, GPU, FPGA and ASIC, and highlight important benchmarking/comparison metrics and design considerations. We will then describe recent techniques that reduce the computation cost of DNNs from both the hardware architecture and network algorithm perspective. Finally, we will discuss the different hardware requirements for inference and training.
Register for the two day short course in Mountain View, California (March 26-27, 2018) here.
An overview paper based on the tutorial "Efficient Processing of Deep Neural Networks: A Tutorial and Survey" is available here.
Entire Tutorial [ slides ]
Entire Tutorial [ slides ]
@article{2017_dnn_piee,
title={Efficient processing of deep neural networks: A tutorial and survey},
author={Sze, Vivienne and Chen, Yu-Hsin and Yang, Tien-Ju and Emer, Joel},
journal={arXiv preprint arXiv:1703.09039},
year={2017}
}