Company executives present Akida, an advanced Spiking Neural Network processor for edge AI applications, at industry’s premier processor event
SAN FRANCISCO-- October 15, 2019 -- BrainChip Holdings Ltd., a leading provider of ultra-low power, high performance edge AI technology, today announced that it will be introducing its AkidaTM Neuromorphic System-on-Chip to audiences at the Linley Fall Processor Conference October 23 and 24 at the Hyatt Regency Hotel in Santa Clara, Calif.
Chief Development Officer Anil Mankar presents “Introducing Akida: An Event-Based Processor” at 2 p.m. on October 24 as part of the event’s session: AI at the Edge. Mankar’s presentation will introduce Akida, an event-based reconfigurable multicore neural-network processor. Akida processes data in the form of spikes, which have a temporal and spatial distribution. Power is consumed only when spikes are processed. The device can run inference for standard convolutional neural networks trained with back-propagation in an event-driven environment. The same device can also use on-chip learning methods to run training and classification for native spiking neural networks, reducing power and memory requirements in edge IoT devices.
“Spiking neural networks have many attractive characteristics, such as rapid learning capabilities, and low compute and memory overhead,” said Mankar. “I am excited to have the opportunity to share with attendees of the Linley Fall Processor Conference how Akida’s capabilities open up hundreds of new possibilities for applications operating at the edge.”
With the proliferation of intelligence into edge devices, there is a new and growing need for fast, small, and power-efficient neural network processors. By performing neural processing and memory accesses on the edge, Akida vastly reduces the computing resources required of the host CPU. This unprecedented efficiency not only delivers faster results, it consumes only a tiny fraction of the power resources of traditional AI processing, reducing the high environmental and economic costs of running hyperscale data centers. Akida is available as an SNN processing chip, as well as a licensable technology that can be integrated with other hardware and devices 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, such as vibrational analysis and keyword spotting, and Industrial Internet-of-Things (IoT).
As the industry's premier processor event, the Linley Fall Processor Conference features technical presentations addressing processors and IP cores for AI applications, embedded, data center, automotive, IoT, and communications. In addition to more than 25 technical presentations by experts from the companies leading the industry, the conference program will include a keynote session covering the latest market, technology, equipment-design, and silicon trends. Additional information about the event is available at https://linleygroup.com/events/event.php?num=47.
About BrainChip Holdings Ltd
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.