Ibase Technology Inc., a provider of embedded computing platforms, is partnering with artificial intelligence chipmaker Hailo to develop new products, according to a company press release. Ibase and Hailo are launching the 11th Gen Intel Core U-Series-based (formerly Tiger Lake) ASB210-953-AI edge AI computing system, targeted at artificial intelligence applications in multiple sectors, including smart cities, industry 4.0 and the automotive industry. "Our collaboration with Hailo enables us to offer customers edge AI inference systems with unprecedented performance and responsiveness," Albert Lee, EVP at Ibase, said in the release. "With a best-in-class AI processor packaged in a module, the Hailo-8 is compatible with NGFF M.2 form factor, as well as with M, B M and A E keys that can be perfectly adopted in our ASB210-953-AI platform to execute real-time deep neural network inferencing in a wide range of market applications." The compact system comes with flexible expansion supporting high-speed 5G connectivity and three M.2 modules.

Learn more about what comes next. Hailo, a startup developing AI accelerator chips for edge devices, today announced that it raised $136 million in a series C funding round led by Poalim Equity and entrepreneur Gil Agmon, with participation from ABB Technology Ventures, Latitude Ventures, OurCrowd, Carasso Motors, Comasco, Shlomo Group, Talcar Corporation, and Automotive Equipment. The capital brings Hailo's total raised to $224 million, and CEO Orr Danon says the company will put the funds toward mass manufacturing of its current chips and the development of next-generation hardware in new and existing markets. AI edge chips are coming into vogue as enterprises begin to realize the benefits of localized computing. Deloitte estimates that in 2020, over 750 million edge AI chips -- chips or parts of chips that perform or accelerate machine learning tasks on-device -- were sold globally.

Munich and Tel Aviv, September 30, 2021 – MicroSys Electronics announced today its partnership with artificial intelligence chipmaker Hailo to launch its miriac AIP-LX2160A embedded platform, hosting up to 5 integrated Hailo-8 AI accelerator modules. The new edge server-grade AI solution enables high-performance and scalable AI inference capabilities at the edge. The new, application-ready AI platform offers industries a high bandwidth and power-efficient solution at the edge for a range of applications in Industry 4.0, such as automotive and heavy machinery. Powered by the NXP QorIQ Layerscape LX2160A high-throughput processor technology, the miriac AIP-LX2160A can integrate multiple advanced Hailo-8 AI accelerators and offers best-in-class processing performance and deep learning capabilities of up to 130 tera-operations per second (TOPS). The combined solution delivers exceptional AI computing performance across multiple standard NN benchmarks, including over 6000 Frames Per Second (FPS) on Resnet-50, over 5000 FPS on Mobilenet-V1 SSD and close to 1000 FPS on YOLOv5m.

Leopard Imaging has been working to address the need for affordable multiprocessing power in deep learning applications. Using Socionext's SC2000 image signal processor and the Hailo-8 M.2 AI acceleration module, Leopard Imaging's EdgeTuring consumes less power, performs at a higher level, and ensures greater reliability for video analytics and privacy at the edge than alternative solutions. Leveraging AWS services such as Amazon Kinesis Video Streams and Amazon Kinesis Data Streams (Amazon KDS), EdgeTuring creates a seamless experience for customers to stream and analyze videos using a simple internet connection. "EdgeTuring has an accuracy ranging from 95% to 99% for several state of the art deep learning – based computer vision applications, such as object detection, image classification and others. Additionally, it processes frames much faster, supports more functions, consumes less power, and costs much less than any comparable solution--all with the ability to stream inputs in real time," said Bill Pu, President and Co-Founder of Leopard Imaging.

Hailo, a Tel Aviv-based startup best known for its high-performance AI chips, today announced the launch of its M.2 and Mini PCIe high-AI acceleration modules. Based around its Hailo-8 chip, these new models are meant to be used in edge devices for anything from smart city and smart home solutions to industrial applications. Today's announcement comes about half a year after the company announced a $60 million Series B funding round. At the time, Hailo said it was raising those new funds to roll out its new AI chips, and with today's announcement, it's making good on this promise. In total, the company has now raised $88 million.

Foxconn, a global leader in smart manufacturing, is joining Socionext, a major provider of advanced SoC solutions for video and imaging systems, and leading artificial intelligence (AI) chipmaker Hailo to launch the next-generation AI processing solution for video analytics at the edge. Foxconn has combined its high-density, fan-less, and highly efficient edge computing solution, "BOXiedge ", with Socionext's high-efficiency parallel processor "SynQuacer " SC2A11, and the Hailo-8 deep learning processor. The new combination provides market-leading energy efficiency for standalone AI inference nodes, benefiting applications including smart cities, smart medical, smart retail, and industrial IoT. In a global AI market forecasted by research firm IDC to approach $98.4 billion in revenue in 2023, this joint solution helps address the need for cost-effective multiprocessing capabilities required in video analytics, image classifications, and object segmentation. The robust, high-efficiency product is capable of processing and analyzing over 20 streaming camera input feeds in real-time, all at the edge.

Hailo, the AI chip startup, is teaming with a Japanese systems designer and manufacturing giant Foxconn to develop an AI edge processor aimed at video analytics applications. The edge partnership is based on Foxconn Technology's BOXiedge platform that integrates the Hailo-8 deep learning processor with a parallel processor from Japanese system-on-chip designer Socienext Inc. The combination creates a local video management server designed to shift workload processing from the cloud to the edge. Yokohama-based Socionext specializes in low-power edge processors, including the recent release of an AI processor based on deep neural network technology. The approach is said to be "quantized," that is, the device is tuned to specific values such as signals, rather than a continuous range of values.

Hailo, an AI startup based in Israel, has released its initial chip that the company claims is "the world's top performing deep learning processor," with the Hailo-8 chip claimed to deliver 26 tera-operations pers second (TOPS), while consuming only a few watts of power. If true, that would certainly put it near or at the top of its class in performance for edge applications in areas like self-driving cars, drones, smart appliances, and virtual/augmented reality devices. The challenge in these edgey environments has always been to get AI processors with the requisite performance for these applications but consuming only the small amounts of power available in these settings. In fact, Hailo is positioning its new offering as chip that "enables edge devices to run sophisticated deep learning applications that could previously run only on the cloud." However, doesn't mean Hailo-8 is as powerful as a top-of-the-line inference GPU for the datacenter.

Deep learning is changing the world around us. To make the greatest impact, Hailo believes deep learning should be physically close to us. To make that happen, we need a new type of computer which can bring intelligence to any product – and this is exactly what we're building. Imagine intelligent devices that are empowered with the performance of a datacenter class computer, operating in real time at reduced power consumption, size and cost. Hailo processors enable edge devices to go beyond handling sensors and streaming volumes of data for remote processing–they can do the processing themselves.