171 citations · 286 across the 25 of their papers we have counts for
11 papers · 1 filter
Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots
Sabrina M. Neuman, Brian Plancher, Bardienus P. Duisterhof +8
Machine learning (ML) has become a pervasive tool across computing systems. An emerging application that stress-tests the challenges of ML system design is tiny robot learning, the…
FRL-FI: Transient Fault Analysis for Federated Reinforcement Learning-Based Navigation Systems
Zishen Wan, Aqeel Anwar, Abdulrahman Mahmoud +4
Swarm intelligence is being increasingly deployed in autonomous systems, such as drones and unmanned vehicles. Federated reinforcement learning (FRL), a key swarm intelligence para…
Widening Access to Applied Machine Learning with TinyML
Vijay Janapa Reddi, Brian Plancher, Susan Kennedy +21
Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation. However, today, most ML resources and experts are siloe…
MLPerf Tiny Benchmark
Colby Banbury, Vijay Janapa Reddi, Peter Torelli +19
Advancements in ultra-low-power tiny machine learning (TinyML) systems promise to unlock an entirely new class of smart applications. However, continued progress is limited by the…
RL-Scope: Cross-Stack Profiling for Deep Reinforcement Learning Workloads
James Gleeson, Srivatsan Krishnan, Moshe Gabel +3
Deep reinforcement learning (RL) has made groundbreaking advancements in robotics, data center management and other applications. Unfortunately, system-level bottlenecks in RL work…
Data Engineering for Everyone
Vijay Janapa Reddi, Greg Diamos, Pete Warden +2
Data engineering is one of the fastest-growing fields within machine learning (ML). As ML becomes more common, the appetite for data grows more ravenous. But ML requires more data…