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Found 9 papers, 5 papers with code
ArchGym: An Open-Source Gymnasium for Machine Learning Assisted Architecture Design
The ease of data collection and aggregation in ArchGym facilitates research in ML-aided architecture design space exploration.
Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration
It is unclear how scalable single-agent formulations are as we increase the complexity of the design space (e. g., full stack System-on-Chip design).
Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots
Machine learning (ML) has become a pervasive tool across computing systems.
Widening Access to Applied Machine Learning with TinyML
Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation.
RL-Scope: Cross-Stack Profiling for Deep Reinforcement Learning Workloads
Deep reinforcement learning (RL) has made groundbreaking advancements in robotics, data center management and other applications.
AutoPilot: Automating SoC Design Space Exploration for SWaP Constrained Autonomous UAVs
Balancing a computing system for a UAV requires considering both the cyber (e. g., sensor rate, compute performance) and physical (e. g., payload weight) characteristics that affect overall performance.
QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement Learning
We believe that this is the first of many future works on enabling computationally energy-efficient and sustainable reinforcement learning.
Learning to Seek: Autonomous Source Seeking with Deep Reinforcement Learning Onboard a Nano Drone Microcontroller
We present fully autonomous source seeking onboard a highly constrained nano quadcopter, by contributing application-specific system and observation feature design to enable inference of a deep-RL policy onboard a nano quadcopter.
Air Learning: A Deep Reinforcement Learning Gym for Autonomous Aerial Robot Visual Navigation
We find that the trajectories on an embedded Ras-Pi are vastly different from those predicted on a high-end desktop system, resulting in up to 40% longer trajectories in one of the environments.
