The cutting-edge brain imaging research carried out by the Centre for Computational Brain Research (CCBR) of the Indian Institute of Technology, Madras (IIT-M) has found a mention at the annual Graphic Processing Units Technology Conference (GTC) of tech giant NVIDIA that was held in the US during March 18-21.

Kimberly Powell, vice president of healthcare at NVIDIA, in her keynote address at GTC, talks about CCBR’s brain research platform called Neuro Voyager. “We have a collaboration with the IIT Madras and their computational brain research. They are announcing this week their brain research platform called Neuro Voyager,” she says, adding, “This team at IIT Madras is imaging the brain at the cellular level. About 100 brains, with each brain image containing between 2-3 petabytes of data. We worked with them to digitise the brain data and visualise it at complete volume, at any kind of slice you wish, any kind of resolution down to half a micron. This is a never before done kind of dataset, this data is so complex, that we can discover new things about the brain that we never had before”.

According to Powell, CCBR is making this available globally to expedite research in the exploration and understanding of the brain.

The computing platform developed by CCBR will store, address, access, process, and visualise such high-resolution digital human brain data at the scale of 100+ petabytes, through nothing more than a web browser interface, a tech blog written by a team of IIT-M and NVIDIA researchers point out.

As part of the collaboration with NVIDIA, CCBR has operationalised a cluster of NVIDIA’s DGX A100 systems to do the complete processing of 10 to 20 brains. As the centre scales to 100 brains and more, the centre looks to a DGX SuperPOD to provide scalable performance, with industry-leading computing, storage, networking, and infrastructure management. With eight NVIDIA A100 Tensor Core GPUs per DGX node, the same data that requires a minimum of 1 hour to detect cells has been reduced to less than 10 minutes on an NVIDIA DGX. This enables whole-brain analysis in a month’s time frame, and scaling to 100 brains becomes practical, the bloggers say.

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