BrainChip CTO Peter van der Made Discusses New Breed of AI Neuromorphic Computing at Two ‘Not-to-be-Missed’ Events
Van der Made to discuss its flagship technology, Akida™, an advanced Spiking Neural Network processor for edge AI applications
SAN FRANCISCO--(BUSINESS WIRE)-- BrainChip Holdings Ltd. (ASX: BRN), a leading provider of ultra-low power, high performance edge AI technology, today announced that Peter Van der Made, CTO and founder of BrainChip, will speak to audiences at two upcoming industry events. Van der Made will discuss its flagship technology, Akida™, an advanced Spiking Neural Network (SNN) processor.
Akida’s AI processing capabilities are used for functions like training, learning, and inferencing and deliver faster results, while consuming only a tiny fraction of the power resources of traditional AI processing. These sessions are intended for anyone interested in discovering more about artificial general intelligence and the most advanced SNN processor on the market today.
Van der Made has been at the forefront of computer innovation for 40 years. His two upcoming appearances include:
Engineer Australia event October 3 at 5 p.m. (AWST)
Engineers Australia WA, Auditorium in West Perth
Perth Machine Learning Group Meetup October 17 at 5:45 p.m. (AWST)
Tank Stream Labs, Ernst and Young Building Level 5 in Perth
Titled “Akida: Event-Based Neural Networks for Fast Inference at the Edge with Low Power Consumption,” Van der Made will address attendees of each event about Akida’s use of SNNs, which offer more rapid training, higher accuracy, and lower compute overhead than traditional neural nets. This is an important feature in the world outside of the Internet, where large datasets are not available.
“There are a number of exciting things I’m looking forward to sharing with attendees of these events about SNNs in general and the opportunities to be gained by real-time learning that are offered by implementing Akida,” said Van der Made. “Applications for Akida are wide ranging – from object detection, face recognition, fault detection and keyword spotting, for instance. Akida learns from experience, autonomously, just like a human, to get us to AGI in a way that deep learning never will.”
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, and Industrial Internet-of-Things (IoT).
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.
Source: BrainChip Holdings Ltd.
Released October 2, 2019