Eyeriss: An Energy-Efficient Reconfigurable Accelerator
for Deep Convolutional Neural Networks
IEEE ISSCC 2016


Email: eyeriss at mit dot edu


Welcome to the Eyeriss Project website!

  • A summary of all related papers can be found here. Other related websites and resources can be found here.
  • or subscribe to our mailing list for updates on the Eyeriss Project.
  • To find out more about other on-going research in the Energy-Efficient Multimedia Systems (EEMS) group at MIT, please go here.

We will be giving a two day short course on Designing Efficient Deep Learning Systems at MIT in Cambridge, MA on July 22-23, 2019. To find out more, please visit MIT Professional Education.


News

  • 5/1/2019

    Eyeriss is highlighted in MIT Technology Review. [ LINK ]

  • 4/21/2019

    Our paper on "Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices" has been accepted for publication in IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS). [ paper PDF | earlier version arXiv ].

  • 07/31/2018

    New paper on "Eyeriss v2: A Flexible and High-Performance Accelerator for Emerging Deep Neural Networks" available on arXiv [ PDF ].

  • 04/17/2018

    New paper on "NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications" will be presented at ECCV 2018. Available on arXiv. [ PDF ].

  • 02/12/2018

    New paper on "Understanding the Limitations of Existing Energy-Efficient Design Approaches for Deep Neural Networks" [ PDF ].

  • 06/25/2017

    Updated slides posted here from ISCA 2017.

  • 03/27/2017

    Updated slides posted here from the CICS/MTL tutorial.

  • 03/27/2017

    New paper on "Efficient Processing of Deep Neural Networks: A Tutorial and Survey" available on arXiv. [ LINK ]

  • 03/25/2017

    DNN Energy Estimation Website available online. [ LINK ]

  • 01/21/2017

    We will be giving a updated version of our tutorial on Hardware Architectures for Deep Neural Networks at ISCA 2017.

  • 01/17/2017

    New paper on “Hardware for Machine Learning: Challenges and Opportunities” will be presented at IEEE CICC 2017. Available on arXiv. [ LINK ]

  • 11/15/2016

    New paper on “Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning” will be presented at CVPR 2017. Available on arXiv. [ LINK ]

  • 11/12/2016

    DNN Tutorial Slides now available online. [ LINK ]

  • 11/08/2016

    Our paper on “Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks” has been accepted for publication in the Journal of Solid-State Circuits (JSSC) Special Issue on the 2016 International Solid State Circuits Conference (ISSCC). [ PDF ]

  • 11/07/2016

    DNN Processor Benchmarking Website available online. [ LINK ]

  • 07/10/2016

    We will be giving a tutorial on Hardware Architectures for Deep Neural Networks at MICRO-49. More info here.

  • 06/21/2016

    Slides from Eyeriss dataflow talk at ACM/IEEE ISCA 2016. [ PDF ]

  • 05/02/2016

    Yu-Hsin to present the work on "Building Energy-Efficient Accelerators for Deep Learning" at Deep Learning Summit Boston 2016.

  • 04/04/2016

    Yu-Hsin presents poster on Eyeriss at GTC 2016.

  • 03/08/2016

    Yu-Hsin to present paper on Eyeriss dataflow design at ACM/IEEE ISCA 2016. [ PDF ]