Revolutionary neuroAIx-Framework speeds up neural simulations 20x!

Published on April 20, 2023

Imagine if you could speed up time and make everything happen 20 times faster! Well, that’s exactly what the neuroAIx-Framework is doing for neural simulations. It’s like having a fast-forward button for your brain! This amazing framework was designed to support the development of next-generation neuroscience simulators that can mimic the complexity of our brain with astonishing speed. By using a cluster of FPGA boards, the neuroAIx-Framework is able to simulate the cortical microcircuit model 20 times faster than real-time! Plus, it’s super energy-efficient, achieving an impressive 48nJ per synaptic event. This groundbreaking technology is a game-changer for computational neuroscience, allowing researchers to explore new concepts and discoveries at an unprecedented pace. If you’re excited about the future of brain simulation and want to learn more about this revolutionary framework, check out the full article!

IntroductionResearch in the field of computational neuroscience relies on highly capable simulation platforms. With real-time capabilities surpassed for established models like the cortical microcircuit, it is time to conceive next-generation systems: neuroscience simulators providing significant acceleration, even for larger networks with natural density, biologically plausible multi-compartment models and the modeling of long-term and structural plasticity.MethodsStressing the need for agility to adapt to new concepts or findings in the domain of neuroscience, we have developed the neuroAIx-Framework consisting of an empirical modeling tool, a virtual prototype, and a cluster of FPGA boards. This framework is designed to support and accelerate the continuous development of such platforms driven by new insights in neuroscience.ResultsBased on design space explorations using this framework, we devised and realized an FPGA cluster consisting of 35 NetFPGA SUME boards.DiscussionThis system functions as an evaluation platform for our framework. At the same time, it resulted in a fully deterministic neuroscience simulation system surpassing the state of the art in both performance and energy efficiency. It is capable of simulating the microcircuit with 20× acceleration compared to biological real-time and achieves an energy efficiency of 48nJ per synaptic event.

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