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 Neur IPS and are eager to show how an Akida-based platform can be utilised to ingest imagery, train the system to recognise 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.”