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.
Eyeriss is highlighted in MIT Technology Review. [ LINK ]
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 ].
New paper on "Eyeriss v2: A Flexible and High-Performance Accelerator for Emerging Deep Neural Networks" available on arXiv [ PDF ].
New paper on "Understanding the Limitations of Existing Energy-Efficient Design Approaches for Deep Neural Networks" [ PDF ].
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 a updated version of our tutorial on Hardware Architectures for Deep Neural Networks at ISCA 2017.
DNN Tutorial Slides now available online. [ LINK ]
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 ]
DNN Processor Benchmarking Website available online. [ LINK ]
Yu-Hsin to present the work on "Building Energy-Efficient Accelerators for Deep Learning" at Deep Learning Summit Boston 2016.
Yu-Hsin presents poster on Eyeriss at GTC 2016.