{"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":"

Neuromorphic computing\u2014a field that applies neuroscience principles to computing systems to mimic the brain\u2019s function and structure\u2014needs to scale up if it is to compete effectively with current computing methods.<\/h2>\n

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

The versatile applications of neuromorphic computing<\/h3>\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

Further extending neuromorphic systems<\/h3>\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>\n

In 2022, a neuromorphic chip designed by a team led by Cauwenberghs showed that these chips could be highly dynamic and versatile without compromising accuracy and efficiency. The NeuRRAM chip<\/a> runs computations directly in memory and can run various AI applications\u2014all at a fraction of the energy consumed by computing platforms for general-purpose AI computing.<\/p>\n

Cauwenberghs explained: \u201cOur research offers a perspective on further extensions of neuromorphic AI systems in silicon and emerging chip technologies to approach both the massive scale and the extreme efficiency of self-learning capacity in the mammalian brain.\u201d<\/p>\n

Mimicking activity in the human brain<\/h3>\n

The authors propose several key features that must be optimised to achieve scale in neuromorphic computing, including sparsity, a defining feature of the human brain.<\/p>\n

The brain develops by forming numerous neural connections (densification) before selectively pruning most of them. This strategy optimises spatial efficiency while retaining information at high fidelity.<\/p>\n

If successfully emulated, this feature could enable neuromorphic systems that are significantly more energy-efficient and compact.<\/p>\n

\u201cThe expandable scalability and superior efficiency derive from massive parallelism and hierarchical structure in the neural representation,\u201d Cauwenberghs said.<\/p>\n

In addition, the authors also call for stronger collaborations within academia and between academia and industry, as well as for developing a wider array of user-friendly programming languages to lower the barrier to entry into the field.<\/p>\n

They believe this would foster increased collaboration, particularly across disciplines and industries.<\/p>\n","protected":false},"excerpt":{"rendered":"

Discover how neuromorphic computing can harness the power of the human brain to compete with other forms of computing.<\/p>\n","protected":false},"author":22,"featured_media":54826,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[830],"tags":[24128,24208],"acf":[],"yoast_head":"\nScaling up neuromorphic computing for more efficient AI<\/title>\n<meta name=\"description\" content=\"Discover how neuromorphic computing can harness the power of the human brain to compete with other forms of computing.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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