Current deep-learning techniques and associated hardware face three main hurdles: first, the economics of Moore’s Law make it very difficult for a start-up to compete in the AI space and therefore is limiting competition. Second, data overflow makes current memory technologies a limiting factor. And third, the exponential increase in computing power requirements has created a “heat wall” for each application.
Meanwhile, the market is demanding more performance for real-time speech recognition and translation, real-time video understanding, and real-time perception for robots and cars, and there are hundreds of other applications asking for more intelligence that combines sensing and computing.
Given these significant hurdles, the time is ripe for disruption: a new technology paradigm in which start-ups can differentiate themselves, and which could utilize the benefits derived from emerging memory technologies and drastically improve data, bandwidth, and power efficiencies. Many foresee this new paradigm to be the neuromorphic approach, some would call it the event-based approach where computation happens only if needed instead of being done at each clock step. This method allows a tremendous energy saving essential to run these greedy and intensive AI algorithms. Yole sees this as the most probable next step in AI technology. Its most recent report represents a window into a possible future where AI uses neuromorphic approaches for sensing and computing.
Yole Développement - www.yole.fr