SANTA CLARA, Calif.--(BUSINESS WIRE)--Crossbar Inc., Gyrfalcon Technology Inc., mtes Neural Networks Corporation (mNN) and Robosensing Inc. today announced an AI consortium that will deliver a vastly accelerated, power-saving AI platform and standard that enables new AI-rich capability for edge computing, gateways, cloud and data centers.
The new group, called SCAiLE (SCalable AI for Learning at the Edge), is already working with Japanese authorities to review opportunities for the 2020 Olympics, including video-based event detection and response capability.
The organization will combine advanced acceleration hardware, resistive memory (ReRAM), optimized neural networks to create ready-made, power-efficient solutions with unsupervised learning and event recognition capability.
The consortium addresses the restrictions of traditional AI methodologies that depend on classification of data. The huge growth of IoT systems including thousands of remote edge devices such as sensor-equipped cameras creates a torrent of unstructured information in multiple forms that pours into cloud-located servers and that cannot be handled effectively by classification alone.
In contrast, SCAiLE will provide for the first time fast, scalable, and power-saving accelerators that use optimized neural networks at the edge to quickly analyze and respond to multi-modal information (including video, images, speech, keywords and sensor feeds). The platform will enable distributed AI applications rather than a centralized cloud system with high latency.
The accelerators will rely heavily on new AI accelerator architectures developed by Gyrfalcon; Crossbar’s massive search across multi-modal datasets based on high bandwidth ReRAM memory; mtes Neural Networks edge and IoT device technology; and Robosensing’s integration of neural network training and optimization. The organization will ensure technical interoperability and quick integration of advanced technologies from SCAiLE members.
“This is a unique opportunity to apply the benefits of ReRAM’s low power and advanced performance advantages, coupled with deep neural network algorithms, to accelerate machine learning and create new exciting applications in AI,” said George Minassian, CEO of Crossbar. “We have been working closely with Gyrfalcon Technology Inc., mtes Neural Networks Corporation and Robosensing, and expect rapid progress in designing the platform.”
“It’s a clear sign that a technology has reached its stride when customers call for inter-operability,” said Jim Handy, an industry analyst at Objective Analysis. “These companies are clearly blazing a trail for others to follow as more and more systems adopt remote processing and AI to filter data so that data centers won’t be overloaded with data from the edge.”
“Combining the high performance with the low power of GTI’s neural net accelerator with the advantages of ReRAM technology from Crossbar, along with the advanced algorithms from Robosensing and mtes Neural Networks will greatly accelerate the adoption of AI at the edge,” said CEO Kimble Dong from GTI. “The results of our collaboration will simplify the development process for new products and provide a unique ability to self-learn at the edge.”
“The large volume of new kinds of information cannot be handled by mere classification,” said Matt Kobayashi, CEO of Robosensing Inc. “We need new ways of handling unstructured data at the edge, and the planned SCAiLE platform can help us get rid of the ‘tyranny of data classification’ through power-saving self-learning devices that use clustering to detect and interpret events.”
“Robosensing is looking forward to being part of SCAiLE to help lead the integration for the next generation of AI learning events. We are positive that this innovative technology will be the next chapter in AI, Learning at the EDGE, and IoT to enable learning for all types of applications.”
“We continue to see a large opportunity for edge-based safety and security devices equipped with event recognition for smart cities,” said Mr. Takaro Harada, CEO of mtes Neural Networks. “SCAiLE offers mNN the ability to create high-performance power-saving capability that can be incorporated into our on-site equipment for both urban and remote areas.”
About SCAiLE, www.scaile.org
SCAiLE is a consortium of leading AI technology providers developing hardware and software solutions for edge and cloud applications. The objective of the consortium is to facilitate the integration of AI-rich capabilities to a variety of devices. The SCAiLE platform and standard will enable edge-based accelerated learning, deep analysis and massive search across multi-modal datasets including video, images, speech, keywords and sensor feeds.
About Crossbar, Inc. www.crossbar-inc.com
Crossbar is the leader in ReRAM technology, enabling kilobytes to terabytes of always-on data storage to be embedded into any processor, microcontroller, FPGA or as a standalone memory chip. Crossbar ReRAM lets designers rethink the compute/storage paradigm, free from the constraints of traditional flash and DRAM memories. From “persistent memory” that brings data closer to CPU to “cognitive memory” that enables in-memory computing without a host CPU, ReRAM is ushering in a new era of data storage and processing for both edge and cloud computing.
About Gyrfalcon Technologies Inc. www.gyrfalcontech.ai
Gyrfalcon Technology Inc. (GTI) is the world’s leading developer of high-performance, low-power and low-cost Artificial Intelligence (AI) accelerators, founded in 2017 by veteran Silicon Valley entrepreneurs and Artificial Intelligence scientists.
About mtes Neural Networks www.mtesnn.jp
mtes Neural Networks Co., Ltd. develops Internet of things (IoT) platforms for businesses to monitor energy health, human health, and structural health. It also provides gateway and sensor node solutions.
About Robosensing Inc. www.robosensing.ai
Robosensing Inc. is a leader in AI optimization and integration, with expertise in unsupervised machine learning and training, multimodal analytics, and advanced AI computing architectures for edge and cloud devices.
