BRAINCHIP ACHIEVES FULL COMMERCIALIZATION OF ITS AKD1000 AIOT CHIP WITH AVAILABILITY OF MINI PCIE BOARDS IN HIGH VOLUME On 17 January 2022 BrainChip announced that it has begun taking orders for the first commercially available Mini PCIe board leveraging its Akida™ advanced neural networking processor, rounding out its suite of AKD1000 offerings.
The AKD1000-powered Mini PCIe boards can be plugged into a developer’s existing system to unlock capabilities for a wide array of edge AI applications, including Smart City, Smart Health, Smart Home and Smart Transportation. BrainChip will also offer the full PCIe design layout files and the bill of materials (BOM) to system integrators and developers to enable them to build their own boards and implement AKD1000 chips in volume as a stand-alone embedded accelerator or as a co-processor.
The new boards help usher in a new era of AI at the edge due to their performance, security, low power requirements, and the ability to perform AI training and learning on the device itself, without dependency on the cloud. The production-ready chips provide high-speed neuromorphic processing of sensor data at a low cost, high speed and very low power consumption. The PCIe boards are immediately available for pre-order on the BrainChip website. Pricing starts at $499.
READ THE FULL RELEASEHERE
VISIT BRAINCHIP SHOP PAGE TO VIEW FULL SUITE OF PRODUCTS AVAILABLE FOR PURCHASEHERE
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MERCEDES-BENZ VISION EQXX CONCEPT
 viaMotortrend
NEUROMORPHIC COMPUTING
Neuro what? Neuromorphic computing is a form of information processing whose hardware runs so-called spiking neural networks. These artificial neural networks mimic natural neural networks, firing only when certain thresholds are reached. Mercedes says the system reduces energy consumption “by orders of magnitude.”
Mercedes engineers worked with California-based artificial-intelligence developer BrainChip to create systems based on the company’s Akida hardware and software. Among other things, the technology makes the “Hey, Mercedes” voice control system in the EQXX five to ten times more efficient than conventional voice control.
Mercedes acknowledges that while neuromorphic computing is still in its infancy, it says similar systems will be available in a variety of consumer products within a few years. And when applied at scale throughout an electric vehicle, neuromorphic computing has the potential to significantly reduce the energy needed to run the latest artificial intelligence technologies.
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AI EVERYWHERE, EVEN IN SURPRISING PLACES WITH BRAINCHIP
BrainChip’s neuromorphic AI technology has long been the talk of the industry, and now the Akida processor is available for purchase.
We invited Rob Telson, VP of Worldwide Sales for BrainChip, to return to the Utilizing AI podcast to give Chris Grundemann and Stephen Foskett an update on the Akida processor. As of today, Akida is available for use by developers and hobbyists to explore neuromorphic compute at the edge. BrainChip enables five sensor modalities: Vision, hearing, touch, olfactory, and taste. BrainChip’s architecture allows incremental on-chip learning at extremely low power, potentially bringing this capability to some surprising places, from home appliances to the factory floor.
Another differentiator of the BrainChip solution is its event-based architecture, which can trigger based on events rather than sending a continual stream of data. As of today, the BrainChip AkidaAKD1000 PCIe development boardis available for purchase so everyone can try out the technology.
LISTEN TO THE FULL PODCAST HERE |
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WEBINAR: DEVELOPING OPTIMIZED SYSTEMS WITH BRAINCHIP'S AKIDA NEUROMORPHIC PROCESSOR 
BrainChip’s Akida processor today finds use in a diversity of applications, such as classifying images, identifying odors and tastes, recognizing breath data for disease classification, identifying air quality, interpreting LiDAR laser light data, recognizing keywords, and detecting cybersecurity attacks. Akida leverages advanced neuromorphic computing as its processing "engine", delivering key features such as one-shot learning and on-device computing with no "cloud" dependencies. As such, it’s particularly valuable in evolving smart “edge” devices, where privacy, security, low power consumption, low latency and high performance, low cost, and small size are all important criteria.
In this session, you'll learn how to easily develop efficient AI in edge devices by implementing Akida IP either into your SoC or as standalone silicon. The webinar is co-presented by Rob Telson, Vice President of Worldwide Sales, and Todd Vierra, Director of Customer Engagement, both of BrainChip. The presenters will provide detailed performance and other results, derived from real-life system implementations using production Akida silicon, as well as share a variety of design techniques and support resources. A question-and-answer session will follow the presentation.
REGISTER HERE |
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(20min delay)