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To improve brain simulation technology, a team of researchers from the University of Sussex developed a GPU-accelerated approach that can generate brain simulation models of almost-unlimited size.

To improve brain simulation technology, a team of researchers from the University of Sussex developed a GPU-accelerated approach that can generate brain simulation models of almost-unlimited size. 

Researchers Dr. James Knight and Thomas Nowotny from the University of Sussex’s School of Engineering and Informatics detailed the work in a paper published in Nature Computational Science journal. 

Using a GPU-accelerated system composed of an NVIDIA TITAN RTX GPU, the team created a cutting-edge model of a Macaque’s visual cortex with 4.13 x 106 neurons and 24.2 x 109 synaptic weights, a simulation that could previously only be done on a supercomputer. 

The neural network-based simulator uses the large amount of computational power of the GPU to procedurally generate connectivity and synaptic weights as spikes are triggered, without having to store connectivity data in memory, the researchers explained. 

“Large-scale simulations of spiking neural network models are an important tool for improving our understanding of the dynamics and ultimately the function of brains. However, even small mammals such as mice have on the order of 1 × 1012 synaptic connections meaning that simulations require several terabytes of data – an unrealistic memory requirement for a single desktop machine,” the researchers explained. 

According to the team, the initialization of the model took six minutes, and the simulation of each biological second took 7.7 min in the ground state, and 8.4 min in the resting state – 35% less time than a previous supercomputer simulation. 

On the software side, the team used the CUDA-based GPU enhanced Neuronal Networks (GeNN) package. GeNN can also be used through external interfaces such as SpineML and SpineCreator, a Python interface (PyGeNN), and a Brian interface via Brian2GeNN.

“This research is a game-changer for computational Neuroscience and AI researchers who can now simulate brain circuits on their local workstations, but it also allows people outside academia to turn their gaming PC into a supercomputer and run large neural networks.”

A pre-print of the paper is available on bioRxiv under open-access terms. The Nature Computational Science paper can be found here.

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NVIDIA Inception partner DarwinAI developed a new AI model to detect COVID-19 in CT scans with 96% accuracy across a wide and diverse number of scenarios. The model, COVID-Net CT-2, was built using a number of large and diverse datasets created over several months with the University of Waterloo and is publicly available on GitHub.

Last year, the startup launched an open source neural network for COVID-19 detection called COVID-Net. Their new model builds upon the initiative with a more robust model, trained on the largest quantity and diversity of multinational patient cases in research literature. Their academic study detailing the construction and validation of the model can be found here.

DarwinAI used an NVIDIA RTX 6000 for training their neural network and the NVIDIA Jetson Nano embedded AI platform to run their inference workloads.

“By building COVID-NET CT-2 from such rich and voluminous data, we’ve been able to achieve a new level of screening accuracy, with COVID-19 sensitivity and positive predictive value exceeding 96% across a wide and diverse number of scenarios,” said Sheldon Fernandez, CEO of Darwin-AI. 

“Our XAI platform was instrumental in the construction of the original COVID-Net model. For this version, we have engaged two senior radiologists in Canada to validate the way in which COVID-Net CT-2 makes its decisions. Much to our delight, both confirmed the decision-making process of COVID-Net CT-2 is consistent with their own expert interpretations,” Fernandez added.  “In addition to illustrating the emerging cooperation between our respective domains, their validation exemplifies the importance of our XAI technology in constructing transparent and trustworthy AI.” 

Both DarwinAI and NVIDIA are enabling researchers to build neural networks to fight COVID-19 with the help of open source AI and publicly available pre-trained models. Medical imaging AI models for detecting COVID-19 in X-rays and CTs can be accessed through DarwinAI’s COVID-Net Initiative and the NVIDIA COVID-19 NGC Catalog.

Learn more about how AI, accelerated computing, and GPU technology are contributing to the worldwide battle against the novel coronavirus in the COVID-19 Research Hub.

For many, the term “autonomous vehicles” conjures up images of self-driving cars. Autonomy, however, is transforming much more than personal transportation. Autonomous trucks are commercial vehicles that use AI to automate everything from shipping yard operations to long-haul deliveries. Due to industry pressures from rising delivery demand and driver shortages, as well as straightforward operational Read article >

The post The Truck Stops Here: How AI Is Creating a New Kind of Commercial Vehicle appeared first on The Official NVIDIA Blog.