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BrainChip Introduces Company's Event-Based Neural-Network IP and NSoC Device at Linley Processor Virtual Conference
AKD1000 is the first event-based processor for Edge AI with ultra-low power consumption and continuous learning
SAN FRANCISCO -- April 02, 2020 -- BrainChip Holdings Ltd. (ASX: BRN), a leading provider of ultra-low power, high performance edge AI technology, today announced that it will be introducing its AKD1000 to audiences at the Linley Fall Processor Virtual Conference on April 6 at 10:00 a.m. Pacific. Interested parties can register here.
Chief Development Officer Anil Mankar presents “Introducing the AKD1000 IP and NSoC for Edge AI IoT” as part of the event’s session: AI for Ultra-Low-Power Applications. Mankar’s presentation will introduce product details of the AKD1000, BrainChip's first event-based neural-network IP and NSoC device, which enables AI capability in edge AI systems at ultra-low power consumption. The AKD1000’s neural processor can run a standard CNN by converting it into event-based, allowing it to implement transfer learning and incremental learning on the chip. The same neural processor can also train and run SNNs natively.
“The AI edge market will be explosive and in order to move analytics to the edge, where data is acquired rather than sending data to the cloud or a data center, solutions will need to be able to be extremely compact and low power,” said Mankar. “Our AKD1000 is able to deliver the portability, connectivity and processing power needed for AI edge applications without retraining the entire network. The Linley Spring Processor Conference is an ideal venue for BrainChip to further detail these advances.”
The BrainChip Akida™ neural processor is the next generation in AI that will enable the edge. It overcomes the limitations of legacy AI chips that require too much power and bandwidth to handle the needs of today’s applications, and moves the technology forward in a significant leap, allowing AI to do more with less.
“AI acceleration is quickly spreading from the cloud to the edge as the rapid adoption of AI across multiple applications and industries is driving the development of a wide variety of AI chips and IP,” said Linley Gwennap, principal analyst and conference chairperson. “This will be our biggest Linley Spring Processor Conference program yet and will showcase the newest AI and processor technologies from established suppliers as well as a host of exciting startups. We’re also looking forward to several new technology and product announcements.”
As the industry's premier processor event, the Linley Spring Processor Conference features in-depth technical presentations addressing processors and IP cores for AI applications, embedded, data center, automotive and communications. This unique forum focuses on the processors and IP cores used in deep learning, embedded, communications, automotive, IoT and server designs. Additional information about the event is available at https://www.linleygroup.com/events/event.php?num=48
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 continuous 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 an event domain neural network processor, 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. The Akida neural processor is designed to provide a complete ultra-low power Edge AI network processor for vision, audio and smart transducer applications. The reduction in system latency provides faster response and a more power efficient system that can help reduce the large carbon footprint of datacenters.
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