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 …

This paper presents the computing system design in our commercial autonomous vehicles,
and provides a detailed performance, energy, and cost analyses. Drawing from our …

Building domain-specific accelerators is becoming increasingly paramount to meet the high-
performance requirements under stringent power and real-time constraints. However …

There has been an arms race for devising accelerators for deep learning in recent years.
However, real-world applications are not only neural networks but often span across …

A key challenge for hardware acceleration of robotics applications is the enormous diversity
of possible deployment scenarios. To create efficient accelerators while minimizing non …

Despite many recent efforts, accelerating robotic computing is still fundamentally
challenging for two reasons. First, robotics software stack is extremely complicated …

Robotics applications have hard time constraints and heavy computational burdens that can
greatly benefit from domain-specific hardware accelerators. For the latency-critical problem …

Computing the gradient of rigid body dynamics is a central operation in many state-of-the-art
planning and control algorithms in robotics. Parallel computing platforms such as GPUs and …

Despite extensive efforts, existing approaches to design accelerators for optimization-based
robotic applications have limitations. Some approaches focus on accelerating general matrix …

We develop and commercialize autonomous machines, such as logistic robots and self-
driving cars, around the globe. A critical challenge to our—and any—autonomous machine …