125 citations · 129 across the 3 of their papers we have counts for
5 papers
Freely scalable and reconfigurable optical hardware for deep learning
Liane Bernstein, Alexander Sludds, Ryan Hamerly +3
As deep neural network (DNN) models grow ever-larger, they can achieve higher accuracy and solve more complex problems. This trend has been enabled by an increase in available comp…
Estimating Silent Data Corruption Rates Using a Two-Level Model
Siva Kumar Sastry Hari, Paolo Rech, Timothy Tsai +7
High-performance and safety-critical system architects must accurately evaluate the application-level silent data corruption (SDC) rates of processors to soft errors. Such an evalu…
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…
SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks
Angshuman Parashar, Minsoo Rhu, Anurag Mukkara +6
Convolutional Neural Networks (CNNs) have emerged as a fundamental technology for machine learning. High performance and extreme energy efficiency are critical for deployments of C…
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…