NeuroMem to Highlight Disruptive AI Technology at EmTech Asia 2018

Press release
Published January 3rd, 2018 - 06:17 GMT
NeuroMem Technologies, has confirmed that its CEO, Prof Pierre Brunswick, will present the company’s disruptive, neuromorphic, AI technology at EmTech Asia 2018.
NeuroMem Technologies, has confirmed that its CEO, Prof Pierre Brunswick, will present the company’s disruptive, neuromorphic, AI technology at EmTech Asia 2018.

NeuroMem Technologies Pte Ltd, a pattern recognition technology company focused on the use and benefits of neuromorphic semiconductor components, has confirmed that its CEO, Prof Pierre Brunswick, will present the company’s disruptive, neuromorphic, AI technology at EmTech Asia 2018, the annual global emerging technologies conference hosted by MIT Technology Review. The conference is scheduled for 30 & 31 January 2018 at Marina Bay Sands Expo and Convention Centre in Singapore. The hottest topics in emerging technology including Artificial Intelligence will be discussed at the show.

Brunswick said, “We understand the high value proposition offered by NeuroMem’s pattern learning and recognition technology and its ability to deploy edge intelligence in a practical way. We are very pleased to be part of an exciting time when new technology is changing the way we increase our productivity and efficiency in business.”

Brunswick will present NeuroMem’s neuromorphic technology showcasing its pattern learning and recognition technology that provides artificial intelligence reactive, on-the-go learning, at high-speed and at low-power. There will be special demonstrations of proof of concept security solutions developed with NeuroMem’s ecosystem partners,

“EmTech Asia 2018 will provide us with the platform to help the community understand the high value proposition of NeuroMem offers in terms of data analytics across multiple sources and applications,” pointed out Brunswick.

Background Information

NeuroMem Technologies

NeuroMem® Technologies Pte Ltd, is licensing NeuroMem IP, which will allow everyday objects to have perception of their environment and interact with users. We truly believe that pattern learning and recognition can become practical and ubiquitous only, if it can rely on components inspired by the human brain, which we call neuromorphic memories merging storage and local processing per cell, and massively parallel interconnected cells operating at low power. 

 

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