According to Andrew Ng, a pioneer in machine learning and co-founder of Google Brain, artificial intelligence will have a transformational impact on the world similar to the electricity revolution. From self-driving vehicles to smart biomedical devices, nearly all future technologies will incorporate algorithms that enable machines to think and act more like humans.
These powerful deep learning algorithms require an immense amount of energy to manipulate, store and transport the data that will make artificial intelligence work. But current computing systems are not designed to support such large network models. Thus, innovative solutions are critical to overcoming performance-energy and hardware challenges, especially for memory.
The need for sustainable computing platforms has motivated Jae-sun Seo and Shimeng Yu, faculty members at Arizona State University and Georgia Tech respectively, to explore emerging memory technologies that will enable parallel neural computing for artificial intelligence.