BrainChip
Adam Osseiran got a PhD degree in microelectronics (1986, Grenoble – France). He was then offered a post-doc at the Swiss Federal Institute of Technology in Lausanne – Switzerland. In 2002, Adam was headhunted to Australia to drive a Major National Research program for the test of integrated circuits.
From 2015 to 2020, Adam was Non-Executive Director at BrainChip. In 2018, together with the Nobel Laureate Professor Barry Marshall, they won the prestigious Western Australia Innovator of the Year Award. Before that, Adam was finalist of the prestigious Western Australia Innovator of the Year Award in 2011, 2012 and 2014 for developments in acoustic sensing technology.
He received many Awards from the IEEE and the Computer Society for his contributions and numerous services to the engineering community. He holds eight patents in the field of design of CMOS structure dedicated to optoelectronics image sensors for automotive applications, systems and sensors for environmental monitoring and in the field of display architecture and medical technology.
In 2014 Adam co-found Innovate Australia with the goal for the nation to become a global leader in innovation by 2030. Adam is an active researcher with main interests in neuromorphic sensing, artificial intelligent systems, environmental monitoring, water quality and sustainable energy systems.
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BrainChip
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BrainChip is a global technology company that has developed a revolutionary advanced neural networking processor that brings artificial intelligence to the edge in a way that existing technologies are not capable. The solution is high performance, small, ultra-low power and enables a wide array of edge capabilities that include local training, learning and inference. The company markets an innovative event-based neural network processor that is inspired by the spiking nature of the human brain and implements the network processor in an industry standard digital process. By mimicking brain processing, BrainChip has pioneered a spiking neural network, called Akida™, which is both scalable and flexible to address the requirements in edge devices. At the edge, sensor inputs are analyzed at the point of acquisition rather than transmission to the cloud or a data center. Akida is designed to provide a complete ultra-low power and fast AI Edge Network for vision, audio, olfactory and smart transducer applications. The reduction in system latency provides faster response and a more power efficient system that can reduce the large carbon footprint data centers.