595 citations · 1.9k across the 91 of their papers we have counts for
11 papers · 1 filter
GLADAS: Gesture Learning for Advanced Driver Assistance Systems
Ethan Shaotran, Jonathan J. Cruz, Vijay Janapa Reddi
Human-computer interaction (HCI) is crucial for the safety of lives as autonomous vehicles (AVs) become commonplace. Yet, little effort has been put toward ensuring that AVs unders…
MLPerf Training Benchmark
Peter Mattson, Christine Cheng, Cody Coleman +34
Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML. But M…
QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement Learning
Srivatsan Krishnan, Maximilian Lam, Sharad Chitlangia +4
Deep reinforcement learning continues to show tremendous potential in achieving task-level autonomy, however, its computational and energy demands remain prohibitively high. In thi…
AdaptivFloat: A Floating-point based Data Type for Resilient Deep Learning Inference
Thierry Tambe, En-Yu Yang, Zishen Wan +5
Conventional hardware-friendly quantization methods, such as fixed-point or integer, tend to perform poorly at very low word sizes as their shrinking dynamic ranges cannot adequate…
Learning to Seek: Autonomous Source Seeking with Deep Reinforcement Learning Onboard a Nano Drone Microcontroller
Bardienus P. Duisterhof, Srivatsan Krishnan, Jonathan J. Cruz +5
We present fully autonomous source seeking onboard a highly constrained nano quadcopter, by contributing application-specific system and observation feature design to enable infere…
Accelerator-level Parallelism
Mark D. Hill, Vijay Janapa Reddi
Future applications demand more performance, but technology advances have been faltering. A promising approach to further improve computer system performance under energy constrain…