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.”
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.”
Arbe from Tel-aviv, announced the closing of $32 million in Round B funding for its 4D Imaging Radar Chipset Solution. Arbe will use the funding to move to full production of its automotive radar chipset, which generates an image 100 times more detailed than any other solution on the market today.
Founded in 2015 by an elite team of semiconductor engineers, radar specialists, and data scientists, Arbe has secured $55 million from leading investors, including Canaan Partners Israel, iAngels, 360 Capital Partners, O.G. Tech Ventures, Catalyst CEL, AI Alliance, BAIC Capital, MissionBlue Capital, and OurCrowd. Arbe is based in Tel Aviv, Israel, and has offices in the United States and China.
The Tel-aviv based company has developed a 4D Imaging Radar Chipset Solution, enabling high-resolution sensing for ADAS and autonomous vehicles. Arbe’s technology produces detailed images, separates, identifies, and tracks objects in high resolution in both azimuth and elevation in a long range and a wide field of view, and complemented by AI-based post-processing and SLAM (simultaneous localization and mapping).
Its Phoenix radar chip supports more than 2000 virtual channels, tracking hundreds of objects simultaneously in a wide field of view at long-range with 30 frames per second of full scan. The company believes its solution pose a low cost alternative to the current LiDAR sensors in ADAS Systems and the future Autonomous Vehicles.
In order to to strengthen its financial results,Panasonic Corp. transfer its unprofitable semiconductor operations to Nuvoton Technology Corporation from Taiwan. The company did not reveal financial details, but Zacks SCR’s analyst, Lisa Thompson, wrote in her update that it is a $250 million in cash deal.
For TowerJazz it is a big news. The Israeli-based semiconductors foundry holds 51% in TPSCo, a company formed by TowerJazz and Panasonic (49%) in April 2014 following the transfer of its semiconductor manufacturing process and capacity tools of 8 inch and 12 inch wafers at its Hokuriku factories (Uozu, Tonami and Arai) to TPSCo.
Panasonic announced that it will transfer the semiconductor business mainly operated by Panasonic Semiconductor Solutions (PSCS), to Nuvoton Technology Corporation, a Taiwan-based semiconductor company owned (62%) by Winbond Electronics. The deal is expected to end in June 2020.
The Competion is “Aggressive”
Panasonic explained in a press release that the competitive environment surrounding the semiconductor business has become extremely severe due to aggressive expansion of competitors, huge investments and industry reorganization through M&A. “In such an environment, the Company has come to believe that the even stronger business operation and the continuous investment is critical in order to achieve a sustained growth and expansion of the semiconductor business. Accordingly, it has concluded that the best option would be to transfer the business to Nuvoton.”
Nuvoton Technology was spun-off as a Winbond affiliate in 2008 while Winbond continues to focus on its large Memory business. Nuvoton provides Microcontrollers, Platform Modules for the PC industry and Mixed Signal devices such as Speech Playback and Record Products with embedded Flash, Audio Power Amplifiers, Audio ADC’s/DAC’s and CODECS. The deal with Panasonic includes technology know how transfer of technologies such as image sensors, battery management and MOSFET for LiB battery circuits protection.
Altair Semiconductor is appointing current VP System Engineering and Product Management Nohik Semel to CEO, to replace outgoing co-founder and CEO Oded Melamed who is stepping down to pursue new startup Opportunity. The appointment will be effective on November 18th, 2019. Semel joined Altair in its very beginning and has spent the last 14 years in different executive positions. He is credited with the development of Altair’s LTE product portfolio.
His accomplishments include developing the ALT1250 and the ALT1160 chipset. ALT1250 is the company’s flagship. It is considered is the smallest and most highly integrated LTE CAT-M and NB-IoT chipset. It features ultra-low power consumption, hardware-based security, and a carrier-grade integrated SIM (iUICC), all 5G ready. ,“We wish Oded the best of luck and expect to see sensational developments from his next project,” said Nohik Semel.
Altair was founded in 2005 by Melamed and the current CTO Yigal Bitran, former Texas Instrument Israel employees, where they led Cable Modem Research and Development. Altair was a pioneer at Cellular IoT chipsets. In 2016 it was acquires by Sony for $212 Million. Altair partners with leading global vendors, including G+D (Giesecke+Devrient), HERE Technologies, Murata, Sierra Wireless and WNC. Its chipsets have been commercially deployed on the LTE networks of AT&T, China Mobile, KDDI, SoftBank, Verizon and Vodafone.
Photo above: A development team at Hailo. The Tel Aviv based company employs around 65 workers today
Tel Aviv-based Hailo is preparing for the mass production of its AI chip that meets the ASIL-B standard of the automotive industry, and start full-scale production during 2020. Company co-founder and CEO Orr Danon told Techtime that the new chip is named Hailo-8 and was developed as part of the cooperation between Hailo and auto manufacturers. The chip enables the meeting of demands for critical life-saving systems, including the meeting of working under conditions of up to 105 degrees Celsius.
According to company data, the Hailo-8 processor reaches up to 26 tera operations per second (TOPS) and 3 TOPS per Watt power efficiency. 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 that improves the image 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 that manages the chip, and the neural network core itself, that is comprised of a flexible matrix of software-configurable processing, controls, computational resources and memory units.
Renew the Old Idea of DFP Processors
Hailo was established in February 2017 by CEO Orr Danon, CTO Avi Baum and the Business Development Manager Hadar Zeitlin. The first investor in the company was Zohar Zisapel, who serves today as Chairman of the board. To date Hailo has raised around $24 million, of which $21 million was in the latest round of financing which was completed in January 2019. The company developed a new architecture for AI chips for edge devices that carry out the execution phase – i.e. the application of the inference of a neural network in edge devices at a rapid speed and with a huge saving in energy. According to Danon, the architecture, which is protected by dozens of pending patents, “belongs to a forgotten family of processors from the Data Flow Processors type.”
In DFP processors, the processing activity takes place only when data is fed into the processor, which conducts a fixed series of operations on top of this information, and then transfers the processed information. “In recent years, it turned out that the reliance on neural networks is an efficient and reliable means of solving many problems, and therefore most of the AI systems that we see in the market are based on neural networks. Here the challenge is structural, since the chip needs to implement a structure of a neural network. In a neural network one infuses experience into the description of a structure, and therefore it is a very efficient solution in solving problems which are based on recognizing examples.”
How is your chip designed? What are the main principles of the architecture?
Danon: “Our architecture describes the structure of the neural network and allocates resources to every layer in the network. We identified that in during the processing of inference, there were differences in the behavior of the various layers of the neural network, and therefore there was a need to provide them with different resources. This runs counter to our competitors, who use solutions such as GPU processors that allocate to each and every layer the same level of resources. Our development software learns the specific problem, characterizes it, and knows how to transfer to the chip instructions on how to manage the resources of each layer in an optimal method.”
What are the components of the chip?
“The idea is to use the memory units that are located very close to the processing units. We allocate memory and processing units in accordance with every task, and in this way achieve very fast processing, and extensive savings in the chip’s power consumption. This allows us to meet the extremely strict standards of the automotive industry, since the chip does not heat up and is capable of operating in the environmental temperatures that the industry demands.”
You claim that your chip is more efficient that other solutions in the market. However, there is no universally accepted means of measuring AI chips.
“We measure the performance of our chips by checking how many operations per Watt we execute a specific neural network. Nowadays there is the MLPerf consortium that is attempting to define a benchmark which will serve as a basis for comparing different deep learning processors. Regarding edge devices, the industry is apparently going in the direction of measuring the number of operations per Watt (TOPS/W) that the neural network carries out for a specific task, like an image.”
Today new methods are being developed for deceiving neural networks. How are you dealing with this problem?
“It is possible to relate to AI deception as a weakness, just as one relates to security vulnerabilities. At the outset, the weaknesses of the software systems surprised the industry, but gradually they found solutions. In the AI field, first of all this is a conceptual problem that lacks a solution on the silicon level. However, if the network was trained in the wrong way, and the attacker is aware of how the network was trained, then he is capable planning an attack. We also deal with this problem, and in principle it shows the advantage of installing AI systems at the edges of the network, since in this way there are less vulnerabilities along the route of transferring information.“
Hailo is growing quickly, and currently employs around 65 workers. The company is in the process of hiring additional manpower. Hailo is focusing its efforts on two key markets: the automotive and IoT. These two markets are expected to be huge and are also very demanding as in both there is a need for a product which is very reliable, low-cost, with very low power consumption. Danon: “In many respects the camera in a vehicle is no different than the IoT camera in a smart city. These are two areas that will be very dominant, and they share many common requirements.”
TowerJazz announced a capacity expansion plan for the Uozu fab in Japan, with total investments of approximately $100 million. The company will add capacity for the 300mm RF SOI process, the 65nm BCD Power Management and the CMOS image sensor platforms. Capacity is targeted to be installed during the first half of 2020. Russell Ellwanger, Chief Executive Officer of TowerJazz, explained: “Our 300mm activities have resulted in strong demand and forecasted excess demand for which we are now investing to fulfill.”
During second quarter of 2019 (ended June 30, 2019), revenues had reached $306 million, reflecting 11% quarter over quarter organic growth (defined as total revenue excluding revenues from Panasonic in the TPSCo fabs and revenues from Maxim in the San Antonio fab). This organic growth of $20 million is offsetting to a great extent the $22 million Panasonic revenue reduction per the revised terms of the contract and a Maxim revenue reduction per the San Antonio fab acquisition agreement.
TowerJazz expects revenues for the third quarter of 2019 to grow to approximately $312 million. Revenues for 2018 were $1.3 billion compared to $1.39 billion in 2017. TowerJazz is a manufacturing services provider of integrated circuits (ICs). Its technology is comprised of SiGe, BiCMOS, mixed-signal/CMOS, RF CMOS, CMOS image sensor, integrated power management (BCD and 700V), and MEMS. TowerJazz operates two manufacturing facilities in Israel (150mm and 200mm), two in the U.S. (200mm) and three facilities in Japan (two 200mm and one 300mm).
Photo above: GlobalFoundries’ Fab-1 in Dresden, Germany
The second largest semiconductor’s foundry (The first is TSMC), GlobalFoundries, announced a departure from the race to achieve smaller transistor nodes. The company said it is putting its 7nm FinFET program on hold indefinitely and restructuring its research and development teams to support its enhanced portfolio initiatives. Mainly shifting the development resources to make its 14/12nm FinFET platform more relevant to the clients, and to add innovative IP and features including RF, embedded memory, low power and more. “This will require a workforce reduction, however a significant number of top technologists will be redeployed on 14/12nm FinFET derivatives and other differentiated offerings.”
The newly appointed CEO of GlobalFoundries, Tom Caulfield, mentioned that the demand for semiconductors has never been higher. But, “The vast majority of today’s fabless customers are looking to get more value out of each technology generation to leverage the substantial investments required to design into each technology node. This industry dynamic has resulted in fewer fabless clients designing into the outer limits of Moore’s Law.”
The ASIC Business will be Independent Company
saicIn addition, GF is establishing its ASIC business as a wholly-owned subsidiary, independent from the foundry business. This independent ASIC entity will provide clients with access to alternative foundry options at 7nm and beyond, while allowing the ASIC business to engage with a broader set of clients, especially the growing number of systems companies that need ASIC capabilities and more manufacturing scale than GF can provide alone.
“Lifting the burden of investing at the leading edge will allow GF to make more targeted investments in technologies that really matter to the majority of chip designers in fast-growing markets such as RF, IoT, 5G, industrial and automotive,” said Samuel Wang, research vice president at Gartner. “While the leading edge gets most of the headlines, fewer customers can afford the transition to 7nm and finer geometries. 14nm and above technologies will continue to be the important demand driver for the foundry business for many years to come.”