April 3, 2017 - In a first for deep learning, an Oak Ridge National Laboratory-led team is bringing together quantum, high-performance and neuromorphic computing architectures to address complex issues that, if resolved, could clear the way for more flexible, efficient technologies in intelligent computing. Deep learning refers to nature-inspired, computer-based technologies that push beyond the conventional binary code, advancing emerging fields such as facial and speech recognition. “Deep learning is transformative,” ORNL’s Thomas Potok said. “Our proposed approach can optimize and manage complexity in a low-power environment, resolving specific challenges when exploring complicated scientific data.” The team’s tri-fold experiment demonstrates the feasibility of using the three architectures in tandem to overcome limitations and represents a new capability not currently available. Details of the team’s experiment are available online.
