A research team from DeepMind introduces Anakin and Sebulba, two architectures that demonstrate reinforcement learning platforms based on TPUs can efficiently deliver exceptional performance at scale and with low cost.
Because training deep learning models requires intensive computation, AI researchers are always on the lookout for new and better hardware and software platforms for their increasingly sophisticated models.
A new paper from Julia Computing Co-Founder and CTO Keno Fischer and Senior Research Engineer Elliot Saba introduces a method and implementation for offloading sections of Machine Learning models written in Julia programming language to TPUs.
ML has revolutionized vision, speech and language understanding and is being applied in many other fields. That’s an extraordinary achievement in the tech’s short history and even more impressive considering there is still no dedicated ML hardware.