NXP and Hailo Expand AI cooperation through MicroSys

NXP Semiconductors and Hailo announced a cooperation to provide joint AI solutions for automotive Electronic Control Units (ECUs). The new solutions will combine NXP’s automotive processors S32G and Layerscape, along with Hailo-8 processor. Hailo-8 is an AI processor for edge computing with up to 26 tera-operations per second (TOPS) at a typical power consumption of 2.5 W. The solutions offer an open software ecosystem for applications and software stacks.

The first solution, powered by the Arm based NXP S32G processor combined with up to two Hailo-8™ AI processors delivering up to 52 TOPS. The second solution, powered by the Arm based NXP Layerscape platform and combined with up to 6 Hailo-8 processors, delivers a high-performance of up to 156 TOPS. “We are excited to partner with a major player like NXP to demonstrate the true potential of AI for automotive,” said Orr Danon, CEO of Hailo.

“We look forward to continuing to work with NXP to expand our edge processing solutions to a broad range of demanding applications including industrial & heavy machinery, robotics, and more.” The NXP-Hailo joint solutions are already being utilized by customers, including MOTER Technologies, which is using the Arm-based NXP S32G processor combined with a Hailo-8 M.2 AI accelerator module for Usage-Based Insurance (UBI) applications.

The evaluation boards were designed and produced by the Germany-based MicroSys, who cooperates with NXP as well as with Hailo. The miriac® AIP-S32G274A and miriac® AIP-LX2160A NXP-Hailo automotive based application-ready platforms are available from MicroSys, as well as development platforms by NXP: BlueBox 3.0 (Layerscape LX2160A and S32G and GoldBox (S32G). Both are compatible with Hailo-8™ M.2 AI Acceleration Modules.

Vanti Brings Analytics to the Electronics Manufacturing

Above: The CEO Smadar David (right) and the CTO Nir Osiroff

AI tecchnologies may have a deep impact on the yield and effectiveness of elelctronics production lines. Vanti Analytics from Tel aviv is doing just that: It provides a SaaS-based solution already tested by adsvanced customer. Today the company announced a $4.5 million seed funding round led by True Ventures and More VC with participation of i3 Equity Partners and the private investor Ariel Maislos. Vanti has raised $6 million total since it was established in 2019.

The company is developing a cloud-based platform that helps manufacturing operations teams increase yields and throughput for electronic products. Its SaaS software platform autonomously leverages machine learning to dramatically reduce ramp-up time, errors and test time for electronics manufacturers. The company was established by the CEO Smadar David and the CTO Nir Osiroff, both are veterans of the automotive LiDAR sensor provider Innoviz Technologies, and of technological units in IDF.

Prior to founding Vanti, Smadar had served as MEMS & Mechanics Group Manager at Innoviz and Osiroff had served as a head of InnovizPro product line at Innoviz. “As a manufacturer in a very competitive environment, we’re always looking to speed up ramp-up time and serve our customers with the highest quality, volume and price,” said Omer Keilaf, CEO and co-founder of Innoviz. “That’s exactly where Vanti’s platform comes into the picture. We liked how fast it was integrated and demonstrated value leveraging our operations data.”

Suffolk County, NY, and Dynamic Infrastructure to maintain dozens of bridges through AI

The New York, Berlin and Tel Aviv based startup Dynamic Infrastructure is expanding its pilot project with the Public Works Department of New York’s Suffolk County using the world’s first deep-learning solution which allows bridge and tunnel owners and operators to obtain visual diagnosis of assets they manage in order to reduce direct and indirect maintenance costs. After the successful completion of a pilot involving one bridge, the parties have agreed to expand the use of the AI-based technology to 74 bridges in the county located on the eastern end of Long Island. Deployment of the technology is currently in process.

Dynamic Infrastructure is currently conducting projects in other states in the U.S. as well as in Germany, Switzerland, Greece, and Israel with private and public transportation bodies. The company’s clients operate a total of 30,000 assets, ranging from national, state, regional and municipal departments of transportation to Public-Private Partnerships (PPPs) and private companies.

“The latest project expansion aims to use our technology to cover the entire inventory by Q2 2021,” said Saar Dickman, Co-founder, and CEO of Dynamic Infrastructure. He added that Suffolk County is typical of the situation in the US at large, where data from the Federal Highway Administration deemed that approximately 30% of all bridges in the US were in fair or poor condition.

AI-based bridge maintenance

According to the American Society of Civil Engineers, which evaluates and publishes a report card on the U.S. infrastructure every four years, the country’s infrastructure was given a D+ grade and more than 56,000 bridges were classified as being “structurally deficient”.

The aim in the deployment in Suffolk County is to enable its Public Works Department to better coordinate and make the right decisions by prioritizing maintenance of its infrastructure assets. “The system allows any operator, inspector or maintenance engineer to have actionable intelligence at their fingertips in order to decide if, when and how the daily maintenance and  maintenance projects should be conducted, by supplying instant alerts about anomalies,” said Kevin Reigrut, member of Dynamic Infrastructure’s board of advisors and former executive director of the Maryland Transportation Authority.

The use of the novel technology translates into a huge annual savings for the Owners, and Operation and Maintenance engineers, and contractors. Dynamic Infrastructure’s AI-based, decision making, SaaS product continuously processes past and current inspection reports and visuals, identifying future maintenance risks and evolving defects. The proprietary technology provides live, cloud-based, risk analysis of any bridge or tunnel and automatically alerts when changes are detected in maintenance and operating conditions—before they develop into large-scale failures.

The platform creates a “visual medical record” for each asset, based on existing images taken from past and current inspection reports and interim inspections. The visual analysis is being done to any visual source, be it smartphones, drones, and laser scanning. The images are compared and serve as the basis for alerts on changes in maintenance conditions. They can be easily accessed through a simple browser and instantly shared with peers and contractors to speed maintenance workflows and improve budget expenditure.

Dynamic Infrastructure harnesses the power of AI to disrupt Operation & Maintenance of critical transportation assets. Founded by industry professionals with decades of operation and maintenance experience for PPPs and DOTs, Dynamic Infrastructure has become an industry leader and key driver of a data revolution in decision-making processes related to bridge and tunnel Operations & Maintenance. Headquartered in New York, NY, with offices in Germany and Israel, Dynamic Infrastructure maintains a close relationship with its clients.

AI-based Visual Assistance Tool for Technicians

TechSee from Tel aviv announced the completion of $30 million equity investment round co-led by OurCrowd, Salesforce Ventures, and TELUS Ventures. Founded in 2015,  the company has raised $54 million in funding to date. TechSee has developed a Computer Vision AI and Augmented Reality solution to assist makers and technicians in the “unboxing” and the installation of electrical and electronics products.

TechSee’s AI platform can automatically identify components, ports, cables, LED indicators, and more to detect issues and suggest resolutions, contact center agents, and field technicians. Via a simple tap of a screen, customers use their smartphone camera to show the virtual technician exactly what they see in their physical environment.

Using Deep Learning, the software (virtual technician) identify the product and visually guide the customer through the unboxing process using a suite of augmented reality tools, including Augmenting guidance for specific components, Tracking consumer and device movements to allow interactive guidance, Providing step-by-step instructions through the installation process and testing to verify the device works properly.

“Our vision is to get rid of the User Manual”

“There has been a significant increase in demand for contactless customer service technologies propelled by COVID-19 and the acceleration of digital transformation projects,” said Eitan Cohen, CEO of TechSee. “Our Visual Automation technology is at the heart of it. Our vision is to get rid of the user manual and replace it with dynamic AR assistants.”

TechSee recently announced a commercial partnership with Verizon to address this issue by bringing visual assistance to customers. It also established commercial partnerships with Vodafone, Orange, Liberty Global, Accenture, Hitachi, and Lavazza. The need to enhance the user’s product unboxing experience has brought many brands to showcase their product unboxing process using video.

In fact, YouTube reports an increase in product unboxing video views of 57% in one year, and an increase in uploads of more than 50%. These videos have more than a billion views annually. Google Consumer Survey underscores these statistics, with 20% of consumers (1 in 5) reporting that they’ve watched an unboxing video.

Hailo Challenges Google and Intel

AI chipmaker Hailo announced the launch of its M.2 and Mini PCIe high-performance AI acceleration modules for empowering edge devices. Integrating the Hailo-8 processor, the modules can be plugged into a variety of edge devices. The modules provides high performance Deep Learning-based applications to edge devices. Hailo’s AI acceleration modules seamlessly integrate into standard frameworks, such as TensorFlow and ONNX, which are both supported by its Dataflow Compiler.

Hailo announced that a comparison between the Hailo-8 average Frames Per Second (FPS) with competitors across multiple standard NN benchmarks shows that Hailo’s AI modules achieve a FPS rate 26x higher than Intel’s Myriad-X modules and 13x higher than Google’s Edge TPU modules. The Hailo-8 M.2 module (photo above) is already integrated into the next generation of Foxconn’s BOXiedge with no redesign required for the PCB.

“Manufacturers across industries understand how crucial it is to integrate AI capabilities into their edge devices,” said Orr Danon, CEO of Hailo. “Simply put, solutions without AI can no longer compete.” The Hailo-8 AI modules are already being integrated by select customers worldwide. More information on the Hailo-8 M.2 and Mini PCIe AI modules can be found here.

Hailo-8 vs. Intel Myriad-X(1) and Google Edge TPU(2) Performance across common Neural Network benchmarks

Foxconn and Hailo to Launch Edge AI Computer

Foxconn has combined its edge computing solution, BOXiedge, with the Japanese Socionext parallel processor SynQuacer” SC2A11, and the Hailo-8 deep learning processor developed by Tel Aviv based chipmaker Hailo. The new device aimed to provide powerful AI processing solution for video analytics at the edge. The new BOXiedge is capable of processing and analyzing over 20 streaming camera input feeds in real-time, all at the edge, including image classification, detection, pose estimation, and other AI-powered applications.

This is a milestone win for Hailo, a chipmaker startup who was established in 2017 and had completed a $60 million financing round in March 2020, with the participation ABB Technology Ventures (VC arm of ABB) and NEC Corporation. The funding will be used to enter mass production of the company’s Hailo-8 Deep Learning chip during 2020. Today the company employs approximately 80 employees.

The company’s Hailo-8 processor is a dedicated Neural Networks processor aimed to implement inference functions on edge devices in Automotive and Industrial applications. Hailo-8 processor reaches up to 26 Tera Operations Per Second (TOPS) and 3 TOPS per Watt. It is comprised of four main components: an Image Signal Processor to improve the image arriving from the sensor before its transfer for processing by the neural network core, an H.264 encoder that handles the video stream, an ARM-M4 processor to manages the chip, and a unique Neural Network core.

Orr Danon, CEO and Co-Founder of Hailo
Orr Danon, CEO and Co-Founder of Hailo

“Our vision is to pave the way for next generation AI solutions,” said Gene Liu, VP of Semiconductor Subgroup at Foxconn Technology Group. “We recognize the great potential in adopting AI solutions for a multitude of applications, such as tumor detection and robotic navigation. This platform will positively impact rapidly evolving sectors including smart cities, smart medical, smart retail, and industrial IoT.”

Foxconn has already deployed several in-house developed AI solutions on different production lines, leading to a reduction of at least one third of the operating costs for appearance defect inspection projects. “We are thrilled to collaborate with two of the global leaders in AI solutions,” said Orr Danon, CEO and Co-Founder of Hailo. “A new generation of chips means a new generation of capabilities at the edge.”

AI Chipmaker Hailo Raised $60 Million

The Tel Aviv based chipmaker startup Hailo, has successfully completed a a $60 million financing round with the participation of key strategic investors ABB Technology Ventures (VC arm of ABB) and NEC Corporation. The funding will be used to enter mass production of the company’s processor Hailo-8 Deep Learning chip during 2020. Today the company employs approximately 80 employees.

Since its inception in February 2017, Hailo had raised $88 million. Following the last investment round, it begins to recruit 30-40 new employees for its research and development and support department, as well as offshore stuff for new offices in Europe, Japan and the US, to be opened in 2020. The company’s Hailo-8 processor is a dedicated Neural Networks processor aimed to implement inference functions on edge devices in Automotive and Industrial applications.

“We look forward to combining Hailo’s solution with our cutting-edge industrial technology as an important piece of the puzzle to drive the digital transformation of industries,” said Kurt Kaltenegger, Head of ABB Technology Ventures. Hiroto Sugahara, General Manager of Corporate Technology Division, NEC Corporation, said that Hailo’s technology will help NEC,  “to dive  deeper into the intelligent video analytics market. We look forward to incorporating Hailo’s technology into our next generation edge-based products.”

CEO, Orr Danon: A novel architecture to enable fast and power saving implementation of Neural Networks
CEO, Orr Danon: A novel architecture to enable fast and power saving implementation of Neural Networks

The Company’s co-founder and CEO, Orr Danon, told Techtime Hailo-8 chip presents a novel architecture to enable fast and power saving implementation of Neural Networks. “We identified that in during the processing of inference, there are differences in the behavior of the different layers in the neural network. Our solution provides the exact resources needed in each layer.

“In contrast, our competitors, who use solutions such as GPU processors, allocate to each and every layer the same level of resources. Our development software learns the specific problem of each application, characterizes it, and give the chip instructions on how to manage the resources of each layer in an optimal method.”

According to Hailo, its processor reaches up to 26 Tera Operations Per Second (TOPS) and 3 TOPS per Watt. It will meet the strict ISO 26262 ASIL-B as well as the AEC Q 100 Grade 2 standards. Hailo-8 is comprised of four main components: an Image Signal Processor to improves the image arriving from the sensor before its transfer for processing by the neural network core, an H.264 encoder that handles the video stream, an ARM-M4 processor to manages the chip, and the neural network core itself.

This neural network is comprised of a flexible matrix of software-configurable processing, controls, computational resources and memory units. “Hailo’s Deep Learning chip is a real game changer in industries such as automotive, industry 4.0, robotics, smart cities, and more,” said Hailo Chairman Zohar Zisapel, “A new age of AI chips means a new age of computing capabilities at the edge.”