{"id":525371,"date":"2025-07-31T16:11:09","date_gmt":"2025-07-31T14:11:09","guid":{"rendered":"https:\/\/silicon-saxony.de\/spinncloud-spinncloud-expands-in-germany-with-leipzig-university-ai-and-hpc-system\/"},"modified":"2025-07-31T14:35:08","modified_gmt":"2025-07-31T12:35:08","slug":"spinncloud-spinncloud-expands-in-germany-with-leipzig-university-ai-and-hpc-system","status":"publish","type":"post","link":"https:\/\/silicon-saxony.de\/en\/spinncloud-spinncloud-expands-in-germany-with-leipzig-university-ai-and-hpc-system\/","title":{"rendered":"SpiNNcloud: SpiNNcloud Expands in Germany with Leipzig University AI and HPC System"},"content":{"rendered":"<p><img decoding=\"async\" style=\"width: 25%;\" src=\"https:\/\/cdn.pblzr.de\/dacbb27c-1270-4041-b681-e2b95f06f8a1\/2025\/03\/spinncloud-logo-400x300_TEXT.jpg\"><\/p>\n<p>Developed by deep-tech company SpiNNcloud, SpiNNcloud Server System can simulate up to a minimum of 10.5 billion neurons for AI, HPC, and other applications. The SpiNNcloud Server System is based on the current chip generation and was pioneered by Steve Furber, designer of the original ARM architecture,and uses a large number of low-power processors for efficiently computing AI and other workloads.<\/p>\n<p>The multi-million Euro system to be deployed in Leipzig is intended for protein folding simulations to achieve personalized medicine. The approach leverages the extreme parallelism and scale of these systems to deploy millions of small models tasked with finding hits between molecules and patient profiles. The system will enable the discovery of new personalized drugs at faster convergence speeds and under a lower energy profile compared to traditional GPU-based systems.<\/p>\n<p>\u201cThe SpiNNcloud Server System architecture makes screening billions of molecules in silico feasible with a brain-inspired supercomputer design,\u201d said Christian Mayr, SpiNNcloud co-founder. \u201cOriginally dedicated to biological neural network simulation, The SpiNNcloud Server System is tailored for massively parallel execution of small, heterogeneous compute workloads, with generally programmable 10 million ARM-based processors with many dedicated DNN accelerators. A prototype neural network allows the screening of 20 billion molecules in less than an hour, two orders of magnitude faster than on 1000 CPU cores.\u201d<\/p>\n<p>\u201cThe extreme parallelism of SpiNNcloud supercomputers makes them ideally suited for protein folding applications like those used in small molecule drug discovery,\u201d said Jens Meiler, Alexander von Humboldt Professor and Director of the Institute for Drug Discovery at Leipzig University. \u201cProtein folding can be viewed as an optimization problem where the protein aims to find its lowest energy state. SpiNNcloud supercomputers are good at solving optimization problems and can be used to find optimal protein structures.\u201d<\/p>\n<p>The SpiNNcloud system employs a highly parallel architecture consisting of 48 SpiNNaker2 chips per server board, each containing 152 Arm-based cores with specialized accelerators. This design enables efficient, event-driven computation, allowing the system to perform complex simulations at a lower energy profile compared to traditional GPU-based systems. Such energy efficiency is crucial for applications where power consumption and cooling are limiting factors.<\/p>\n<p>\u201cOur brain-inspired computing architecture is uniquely suited for deploying efficient algorithms that require dynamic sparsity and extreme parallelism,\u201d said Hector Gonzalez, SpiNNcloud co-founder and CEO. \u201cOur systems are 18 times more energy efficient than current GPUs and are being used by leading institutions across Europe and the US. The deployment in Leipzig demonstrates the flexibility of our systems, as well as the continued adoption of the technology for unrivaled performance and energy efficiency.\u201d<\/p>\n<p>Looking ahead from a broader perspective, SpiNNcloud is enabling support for the next generation of Gen AI algorithms, paving a radically more efficient path to machine learning advancement through dynamic sparsity. Recent breakthroughs in machine learning are driving a transition from traditional dense modeling \u2013 centered on fixed feature selection within neural representations \u2013 to extreme dynamic sparsity, where a subset of neural pathways are selectively activated based on the input. This approach helps to shape entirely new architectures for AI foundation models, and addresses the current energy crisis driven by mainstream AI scaling trends.<\/p>\n<p>\u201cSpiNNcloud\u2019s approach reflects a broader shift in performance-intensive computing, where innovation demands that infrastructure and algorithms be co-designed from the ground up,\u201d said Peter Rutten, Research Vice-President, Performance Intensive Computing, Worldwide Infrastructure Research at IDC.<\/p>\n<p>For more information about SpiNNcloud and the SpiNNcloud platform, please visit <a href=\"https:\/\/spinncloud.com\" target=\"_blank\">https:\/\/spinncloud.com<\/a>.<\/p>\n<p>About SpiNNcloud<\/p>\n<p>SpiNNcloud enables customers to realize complex AI-driven systems through brain-inspired supercomputing technology. Our SpiNNaker2 architecture supports dynamic sparsity in mainstream AI applications while delivering superior energy efficiency. As a winner of the prestigious EIC Accelerator program, SpiNNcloud continues to solidify itself as sole European champion for specialized AI Supercomputers.<\/p>\n<h3 class=\"\">Further Links:<\/h3>\n<p>\ud83d\udc49<a href=\"https:\/\/spinncloud.com\" target=\"_blank\">https:\/\/spinncloud.com<\/a><a href=\"https:\/\/spinncloud.com\" target=\"_blank\"><\/a><\/p>\n<p><i>Photo: SpiNNcloud<\/i><\/p>\n","protected":false},"excerpt":{"rendered":"<p>July 28, 2025. SpiNNcloud today secured a deal to deliver a \u201cbrain-inspired\u201d supercomputer to Leipzig University, marking a significant milestone in the advancement of computing for small molecule research for personalized medicine. This is the largest SpiNNcloud System to be deployed specifically for drug discovery, encompassing around 650k cores.<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[4818],"tags":[1966,11377,4819,4820,1974,1960],"class_list":["post-525371","post","type-post","status-publish","format-standard","hentry","category-microelectronics","tag-artificial-intelligence","tag-halbleiter-en","tag-kunstliche-intelligenz-ki","tag-neuromorphes-computing-en","tag-neuromorphic-computing","tag-semiconductor"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>SpiNNcloud: SpiNNcloud Expands in Germany with Leipzig University AI and HPC System - Silicon Saxony<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/silicon-saxony.de\/en\/spinncloud-spinncloud-expands-in-germany-with-leipzig-university-ai-and-hpc-system\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"SpiNNcloud: SpiNNcloud Expands in Germany with Leipzig University AI and HPC System - Silicon Saxony\" \/>\n<meta property=\"og:description\" content=\"July 28, 2025. 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