2021-02-01

GML and DMP Partner to Showcase Virtual AI Sports Coach at docomo Open House

(Paris, Tokyo) February 1, 2021 – GrAI Matter Labs (GML), a pioneer of brain-inspired ultra-low latency computing, and Digital Media Professionals (DMP), a leading developer of Edge AI solutions, today announced the companies’ partnership to showcase a novel virtual AI Sports Coaching Application at the docomo Open House 2021^* to be held online from February 4 to 7.

The partnership combines the companies’ expertise and technology in Edge AI systems and applications. Through the agreement, DMP and GML will develop event-based edge processing solutions to deliver exciting experiences that meet the demand of ultra-fast response applications at low power. As an example, at the upcoming docomo Open House 2021, the companies will showcase a virtual AI sports coach that helps golf enthusiasts to improve their swing using Pose estimation.

Pose estimation analysis of humans can track every small movement and do a biomechanical study in real-time. Leveraging GML’s GrAI VIP Vision Inference Processor and DMP’s edge AI processing algorithms, a new class of virtual assistants becomes possible that react in real-time and enable pose-based applications in sports analysis, assisted living, industrial automation, surveillance, and more.

“Our GrAI VIP Vision Inference Processor will leverage latency and performance leadership to revolutionize virtual assistants across the edge,” said Ingolf Held, CEO of GrAI Matter Labs.
“We are thrilled to announce our partnership with DMP and look forward to jointly bring novel experiences to platforms from smartphones to smarter robotics in the coming years.”

“Combining DMP’s Edge AI solution with the GrAI VIP Vision Inference Processor will dramatically improve the cost and power in the Edge AI space.” said Tatsuo Yamamoto, Chairman & CEO of DMP.
“The AI Sports Coaching Application is a perfect example of exploiting sparsity (spatial and temporal) in the input data to reduce the compute at the edge and provide fast inferences at low power. “