{"id":54825,"date":"2025-01-24T11:41:02","date_gmt":"2025-01-24T11:41:02","guid":{"rendered":"https:\/\/www.innovationnewsnetwork.com\/?p=54825"},"modified":"2025-01-24T11:41:02","modified_gmt":"2025-01-24T11:41:02","slug":"scaling-up-neuromorphic-computing-for-more-efficient-and-effective-ai","status":"publish","type":"post","link":"https:\/\/www.innovationnewsnetwork.com\/scaling-up-neuromorphic-computing-for-more-efficient-and-effective-ai\/54825\/","title":{"rendered":"Scaling up neuromorphic computing for more efficient and effective AI"},"content":{"rendered":"
Researchers have presented a detailed roadmap of how neuromorphic computing can reach this goal.<\/p>\n
The research offers a new and practical perspective toward approaching the cognitive capacity of the human brain with comparable form factors and power consumption.<\/p>\n
\u201cWe do not anticipate that there will be a one-size-fits-all solution for neuromorphic systems at scale but rather a range of neuromorphic hardware solutions with different characteristics based on application needs,\u201d the authors stated.<\/p>\n
Neuromorphic computing has applications in scientific computing, artificial intelligence, augmented and virtual reality, wearables, smart farming, smart cities, and more.<\/p>\n
Neuromorphic chips have the potential to outpace traditional computers in energy and space efficiency and performance. This could present substantial advantages across various domains, including AI, healthcare, and robotics.<\/p>\n
As AI’s electricity consumption<\/a> is projected to double by 2026, neuromorphic computing emerges as a promising solution.<\/p>\n \u201cNeuromorphic computing is particularly relevant today when we are witnessing the untenable scaling of power- and resource-hungry AI systems,\u201d said Gert Cauwenberghs, a Distinguished Professor in the UC San Diego Shu Chien-Gene Lay Department of Bioengineering and one of the paper\u2019s co-authors.<\/p>\n Neuromorphic computing is at a pivotal moment, said Dhireesha Kudithipudi, the Robert F. McDermott Endowed Chair at the University of Texas San Antonio and the paper\u2019s corresponding author.<\/p>\n \u201cWe are now at a point where there is a tremendous opportunity to build new architectures and open frameworks that can be deployed in commercial applications,\u201d she said.<\/p>\n \u201cI strongly believe that fostering tight collaboration between industry and academia is the key to shaping the future of this field.\u201d<\/p>\n Last year, Cauwenberghs and Kudithipudi secured a $4 million grant from the National Science Foundation to launch THOR: The Neuromorphic Commons<\/a>, a first-of-its-kind research network providing access to open neuromorphic computing hardware and tools in support of interdisciplinary and collaborative research.<\/p>\nFurther extending neuromorphic systems<\/h3>\n