192 citations · 252 across the 5 of their papers we have counts for
5 papers
Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices
Yu-Hsin Chen, Tien-Ju Yang, Joel Emer +1
A recent trend in DNN development is to extend the reach of deep learning applications to platforms that are more resource and energy constrained, e.g., mobile devices. These endea…
Towards Closing the Energy Gap Between HOG and CNN Features for Embedded Vision
Amr Suleiman, Yu-Hsin Chen, Joel Emer +1
Computer vision enables a wide range of applications in robotics/drones, self-driving cars, smart Internet of Things, and portable/wearable electronics. For many of these applicati…
Efficient Processing of Deep Neural Networks: A Tutorial and Survey
Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang +1
Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs del…
Hardware for Machine Learning: Challenges and Opportunities
Vivienne Sze, Yu-Hsin Chen, Joel Emer +2
Machine learning plays a critical role in extracting meaningful information out of the zetabytes of sensor data collected every day. For some applications, the goal is to analyze a…
Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning
Tien-Ju Yang, Yu-Hsin Chen, Vivienne Sze
Deep convolutional neural networks (CNNs) are indispensable to state-of-the-art computer vision algorithms. However, they are still rarely deployed on battery-powered mobile device…