The brain and all its magnificent capabilities is powered by less than 20 watts. Stop to think about that for a second. As I write this blog my laptop is using about 80 watts, yet at only a fourth of the power, our brain outperforms state-of-the-art supercomputers by several orders of magnitude when it comes to energy efficiency and volume. For this reason it shouldn't be surprising that scientists around the world are seeking inspiration from the human brain as a promising avenue towards the development of next generation AI computing systems and while the IT industry has made significant progress in the past several years, particularly in using machine learning for computer vision and speech recognition, current technology is hitting a wall when it comes to deep neural networks matching the power efficiency of their biological counterpart, but this could be about to change. As reported last week in Nature Communications, my colleagues and I at IBM Research and collaborators at EPFL and the New Jersey Institute of Technology have developed and experimentally tested an artificial synapse architecture using 1 million devices--a significant step towards realizing large-scale and energy efficient neuromorphic computing technology.
Jul-9-2018, 12:36:55 GMT