192 citations · 731 across the 30 of their papers we have counts for
5 papers · 1 filter
Efficient and Robust Machine Learning for Real-World Systems
Franz Pernkopf, Wolfgang Roth, Matthias Zoehrer +7
While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation and the vision of the Internet-of-Things fuel the interest in resource ef…
Energy Efficient Hardware for On-Device CNN Inference via Transfer Learning
Paul Whatmough, Chuteng Zhou, Patrick Hansen +1
On-device CNN inference for real-time computer vision applications can result in computational demands that far exceed the energy budgets of mobile devices. This paper proposes Fix…
SCALE-Sim: Systolic CNN Accelerator Simulator
Ananda Samajdar, Yuhao Zhu, Paul Whatmough +2
Systolic Arrays are one of the most popular compute substrates within Deep Learning accelerators today, as they provide extremely high efficiency for running dense matrix multiplic…
Euphrates: Algorithm-SoC Co-Design for Low-Power Mobile Continuous Vision
Yuhao Zhu, Anand Samajdar, Matthew Mattina +1
Continuous computer vision (CV) tasks increasingly rely on convolutional neural networks (CNN). However, CNNs have massive compute demands that far exceed the performance and energ…
Mobile Machine Learning Hardware at ARM: A Systems-on-Chip (SoC) Perspective
Yuhao Zhu, Matthew Mattina, Paul Whatmough
Machine learning is playing an increasingly significant role in emerging mobile application domains such as AR/VR, ADAS, etc. Accordingly, hardware architects have designed customi…