ALISO VIEJO, Calif.-- April 22, 2021--BrainChip Holdings Ltd. (ASX: BRN), a leading provider of ultra-low power, high-performance AI technology, introduced MetaTF, a versatile ML framework that allows people working in the convolutional neural network space to quickly and easily move to neuromorphic computing without having to learn anything new, today at Linley Spring Conference 2021.
The MetaTF development environment is an easy-to-use, complete machine learning framework for the creation, training and testing of neural networks, supporting the development of systems for Edge AI on BrainChip’s Akida event domain neural processor. The MetaTF development environment leverages TensorFlow and Keras for industry-standard neural network development and training and includes the Akida Execution Engine (chip simulator), data-to-event converters, and a model zoo of pre-trained models. The framework leverages the Python scripting language and its associated tools and libraries, including Jupyter notebooks and NumPy.
Deep-learning professionals do not need to learn a new framework to start using MetaTF immediately. In three steps, MetaTF users can go from designing and training CNNs to converting them for deployment on the Akida neural processor to fully leverage neuromorphic computing and overcome the challenges of AI at the Edge. By minimizing complexity and reducing wasted time in development, BrainChip enables organizations to maximize resources and minimize project times for greater ROI.
“AI doesn’t have to be complex and people don’t have to know how to program neuromorphic computing to take advantage of its benefits,” said Anil Mankar, Chief Development Officer at BrainChip. “The future is SNN and we’ve built an easy way to get there. With MetaTF, we introduce another piece of the puzzle that allow users to quickly and easily train, convert and deploy ML models to Akida while working in their current software environments. MetaTF enables the future today.”
Akida neuromorphic processors are revolutionary advanced neural networking processors that bring 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. The Akida (NSoC) and intellectual property can be used in applications including Smart Home, Smart Health, Smart City and Smart Transportation. These applications include but are not limited to home automation and remote controls, industrial IoT, robotics, security cameras, sensors, unmanned aircraft, autonomous vehicles, medical instruments, object detection, sound detection, odor and taste detection, gesture control and cybersecurity. The Akida NSoC is designed for use as a stand-alone embedded accelerator or as a co-processor, and includes interfaces for ADAS sensors, audio sensors, and other IoT sensors. Akida brings AI processing capability to edge devices for learning, enabling personalization of products without the need for retraining
Those interested in learning more about the MetaTF development environment can visit https://doc.brainchipinc.com/.
About BrainChip Holdings Ltd (ASX: BRN)
BrainChip is a global technology company that is producing a groundbreaking neuromorphic processor that brings artificial intelligence to the edge in a way that is beyond the capabilities of other products. The chip is high performance, small, ultra-low power and enables a wide array of edge capabilities that include on-chip training, learning and inference. The event-based neural network processor is inspired by the spiking nature of the human brain and is implemented in an industry standard digital process. By mimicking brain processing, BrainChip has pioneered a processing architecture, 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 through transmission via the cloud to a data center. Akida is designed to provide a complete ultra-low power and fast AI Edge Network for vision, audio, olfactory 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 of data centers.
Additional information is available at https://www.brainchipinc.com