with an open-source, community-driven neuromorphic computing framework Taking Neuromorphic Computing to the Next Level with Loihi 2
Many emerging AI applications—especially those that must operate in unpredictable real-world environments with power, latency, and data constraints—require fundamentally new approaches and deep neural networks are needed to address these challenges.
Abstract
Recent breakthroughs in AI have swelled our appetite for intelligence in computing devices at all scales and form factors. This new intelligence ranges from recommendation systems, automated call centers, and gaming systems in the data center to autonomous vehicles and robots to more intuitive and predictive interfacing with our personal computing devices to smart city and road infrastructure that immediately responds to emergencies. Meanwhile, as today’s AI technology matures, a clear view of its limitations is emerging. While deep neural networks (DNNs) demonstrate a near limitless capacity to scale to solve large problems, these gains come at a very high price in computational power and pre-collected data. Many emerging AI applications—especially those that must operate in unpredictable real-world environments with power, latency, and data constraints—require fundamentally new approaches.