Latest

• Datasheets for Machine Learning Sensors
• Materiality and Risk in the Age of Pervasive AI Sensors
• Invited: The Magnificent Seven Challenges and Opportunities in Domain-Specific Accelerator Design for Autonomous Systems
• RobotPerf: An Open-Source, Vendor-Agnostic, Benchmarking Suite for Evaluating Robotics Computing System Performance
• TinyML4D: Scaling Embedded Machine Learning Education in the Developing World
• AI in the Developing World: How ‘Tiny Machine Learning’ can have a Big Impact
• RoboShape: Using Topology Patterns to Scalably and Flexibly Deploy Accelerators Across Robots
• Bridging the Digital Divide: the Promising Impact of TinyML for Developing Countries
• Machine Learning Sensors: A Design Paradigm for the Future of Intelligent Sensors
• Is TinyML Sustainable? Assessing the Environmental Impacts of Machine Learning on Microcontrollers
• RobotCore: An Open Architecture for Hardware Acceleration in ROS 2
• Closing the Sim-to-Real Gap for Ultra-Low-Cost, Resource-Constrained, Quadruped Robot Platforms
• Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots
• Machine Learning Sensors
• TinyML: Applied AI for Development
• TinyMLedu: The Tiny Machine Learning Open Education Initiative
• GRiD: GPU-Accelerated Rigid Body Dynamics with Analytical Gradients
• Widening Access to Applied Machine Learning with TinyML
• RoboRun: A Robot Runtime to Exploit Spatial Heterogeneity
• The Role of Compute in Autonomous Aerial Vehicles
• Robomorphic Computing: A Design Methodology for Domain-Specific Accelerators Parameterized by Robot Morphology
• Accelerating Robot Dynamics Gradients on a CPU, GPU, and FPGA