SAN FRANCISCO--(BUSINESS WIRE)--BrainChip Holdings Ltd. (ASX: BRN), a leading provider of ultra-low power, high performance edge AI technology, today announced that it has been accepted to present a demonstration featuring its Akida™ Neuromorphic System-on-Chip Technology, recognizing and classifying hand gestures from the audience at the 33rd Conference on Neural Information Processing Systems (NeurIPS), at the Vancouver Convention Center in Vancouver, Canada.
The paper titled “Human Gesture Recognition using Spiking Input on Akida Neuromorphic Platform” co-authored by Sounak Dey, Arijit Mukherjee from TCS Research at Tata Consultancy Services (TCS) and Gilles Bézard, Douglas McLelland from BrainChip will be presented. The demonstration will involve capturing a few hand gestures and hand positions from the audience using a Dynamic Vision Sensor camera and performing live learning and classification using the Akida neuromorphic platform. This will showcase the fast and lightweight unsupervised live learning capability of the spiking neural network (SNN) and the Akida neuromorphic chip, which takes much less data than a traditional deep neural network (DNN) counterpart, consuming much less power during training.
Akida is available as a licensable IP technology that can be integrated into ASIC devices and will be available as an integrated SoC, both suitable for applications such as surveillance, advanced driver assistance systems (ADAS), autonomous vehicles (AV), vision guided robotics, drones, augmented and virtual reality (AR/VR), acoustic analysis, and Industrial Internet-of-Things (IoT). Akida performs neural processing and memory accesses on the edge, which vastly reduces the computing resources required of the system host CPU. This unprecedented efficiency not only delivers faster results, it consumes only a tiny fraction of the power resources of traditional AI processing. Functions like training, learning, and inferencing are orders of magnitude more efficient with Akida.
“While the recognition of a simple hand gesture might at first seem simple, it is in fact quite a revolutionary advance in the state of today’s human and robot interactive environments,” said Roger Levinson, COO of BrainChip. “We are pleased to have the opportunity to showcase the extent of this revolutionary advance at NeurIPS and are eager to show how an Akida-based platform can be utilized to ingest imagery, train the system to recognize what it has seen and learn in a way that is vastly more efficient and accurate than what other solutions have been able to achieve thus far.”
NeurIPS is held to foster the exchange of research on neural information processing systems in their biological, technological, mathematical, and theoretical aspects. The core focus is peer-reviewed novel research which is presented and discussed in the general session, along with invited talks by leaders in their field. The weeklong event takes place December 8-14 and features tutorials, demonstrations, meetings and a full-day industry expo. Additional information about NeurIPS is available at https://nips.cc/Conferences/2019
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About BrainChip Holdings Ltd (ASX: BRN)
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 datacenter. Akida is designed to provide a complete ultra-low power AI Edge Network for vision, audio 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 datacenters. Additional information is available at https://www.brainchipinc.com.