Intel Corporation announced that it has acquired Habana Labs, an Israel-based developer of programmable deep learning accelerators for approximately $2 billion. Habana will continue to be based in Israel where Intel also has a significant presence and long history of investment. Prior to this transaction, Intel Capital was an investor in Habana. Habana chairman Avigdor Willenz will serve as a senior adviser to Intel.
“Habana turbo-charges our AI offerings for the data center,” said Navin Shenoy, general manager of the Data Platforms Group at Intel. “This acquisition advances our AI strategy, which is to provide solutions to fit every performance need – from the intelligent edge to the data center. Our combined IP and expertise will deliver unmatched computing performance and efficiency for AI workloads in the data center.”
Intel expects that the fast-growing AI silicon market be greater than $25 billion by 2024, and within that, AI silicon in the data center is expected to be greater than $10 billion. In 2019, Intel expects to generate over $3.5 billion in AI-driven revenue, up more than 20 percent year-over-year.
Based in Caesarea, Israel, Habana labs was established in 2016 with Willenz as its first investor, and has developed dedicated chips for Deep Learning Training and Inference. Its Goya AI Inference Processor, which is commercially available, has demonstrated excellent inference performance including throughput and real-time latency in a highly competitive power envelope.
The Gaudi AI Training Processor is currently sampling with select hyperscale customers. Large-node training systems based on Gaudi are expected to deliver up to a 4x increase in throughput versus systems built with the equivalent number of GPUs. It is produced in TSMC’s 16 manometer process.
The acquisition gives Habana access to Intel AI capabilities, including deep expertise in AI software, algorithms and research that will help Habana scale and accelerate. In November 2018, Intel Capital led a $75 million investment round in Habana Labs. “We have been fortunate to get to know and collaborate with Intel given its investment in Habana, and we’re thrilled to be officially joining the team,” said David Dahan, CEO of Habana.
Last week, Intel and Microsoft brought together nearly 100 security and Artificial Intelligence (AI) experts to discuss new standards for Homomorphic Encryption (HE), which is emerging as a leading method to protect privacy in machine learning and cloud computing. The HE standards workshop took place on Intel’s Santa Clara, California campus. Following the first meeting in October, 2018, Intel and Microsoft initiated the founding of the HomomorphicEncryption.org group.
As more data is collected and used to power AI systems, concerns about privacy are on the rise. Casimir Wierzynski from the office of the CTO of AI Products Group at Intel, said that Intel is collaborating with Microsoft Research and Duality Technologies on standardizing HE, “to unlock the power of AI while still protecting data privacy.”
Fully homomorphic encryption, or simply homomorphic encryption, refers to a class of encryption methods envisioned by Rivest, Adleman, and Dertouzos already in 1978, and first constructed by Craig Gentry in 2009. Homomorphic encryption differs from typical encryption methods in that it allows computation to be performed directly on encrypted data without requiring access to a secret key. The result of such a computation remains in encrypted form, and can at a later point be revealed by the owner of the secret key.
It allows AI computation on encrypted data, thus enabling data scientists and researchers to gain valuable insights without decrypting or exposing the underlying data or models. This is particularly useful in instances where data may be sensitive – such as with medical or financial data. Homomorphic encryption also enables training models directly on encrypted data, without exposing its content. Such encryption would enable researchers to operate on data in a secure and private way, while still delivering insightful results.
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.”
A recent report made by BIS Research, Elbit Systems from Israel is mentioned as one of the 10 key players in emerging global Cognitive Electronic Warfare systems market. BIS estimates that the market will witness high growth during 2023-2028, as cognitive electronic warfare systems have the capability to help overcome the problem of reprogramming the electronic warfare models.
“Artificial Intelligence and Machine Learning with computational hardware are likely to revolutionize the electronic warfare (EW) systems.” According to BIS Research analysis, the global cognitive electronic warfare system market is expected to report a revenue of $385.7 million in 2023 and is estimated to cross $928.4 million by 2028, at a CAGR of 19.20% during 2023-2028.
The analysts indicate that since cognitive EW systems make use of AI and machine learning techniques, they are expected to overcome most of the drawbacks of current electronic warfare system, such as incapability of handling excessive load and being non-responsive to unknown threats. This market is expected to witness a high growth rate owing to countries such as the U.S., Russia, and China, who plan to implement artificial intelligence (AI) in the military.
A European Surprise in the Market
AI is required for handling the data and information of huge sensors and communication networks, as the quantity of information is increasing. AI and machine learning offer potential benefits to military organizations by providing fast and high-quality data, which can support in deep analysis of complex and strategic data. Europe, including some of the major companies such as the U.K, Brazil, Russia, Germany, and France is the most prominent region for the cognitive electronic warfare systems market.
North America is the second most prominent regions, which is continuously progressing in the field of cognitive electronic warfare systems market. According to Swati Chaturvedi, Senior Research Analyst at BIS Research, Europe is one of the most prominent regions for the growth of the global cognitive EW market and will dominate it in 2023. “Europe is keenly focusing and investing in the research and development of AI for military.
“However, the geographical analysis of this market unveils an immense potential for its growth in the region of North America. Similarly, the Asia-Pacific market is also likely to witness numerous growth opportunities during the forecast period 2023-2028.” According to BIS Research, the 10 key players in the global cognitive electronic warfare system market are BAE Systems, Elbit Systems, General Dynamics, Harris Corporation, Leonardo S.p.A., Lockheed Martin, Raytheon Company, Saab AB, Textron Inc., and Thales Group.
Hailo from Tel Aviv announced the the first samples of its Hailo-8 Deep Learning Processor were delivered to selected partners, mainly from the automotive industry. To allow running deep learning applications on the edge devices, Hailo re-designed the main pillars of computer architecture: memory, control, and compute and added a comprehensive Software Development Kit (SDK) co-developed with the hardware.
The result is a 16 nano-meter chip produced by TSMC capable of delivering up to 26 Tera Operations Per Second (TOPS). The company said that during ResNet-50 benchmark tests, Hailo-8 outperformed Nvidia’s Xavier AGX and consumed almost 20 times less power while performing the same tasks. Hailo was established in 2017 by former members of the Israel Defense Forces’ intelligence unit.
AI Beyond the Cloud
Its deep learning processor is designed to run complex algorithms on autonomous vehicles, smart cameras, smartphones, drones, AR/VR platforms, and wearable devices. The company said it is now working in cooperation with tier-1 automotive companies on advanced driver-assistance systems (ADAS), and with major players in smart city and smart home markets, to empower powerful IoT devices.
“In recent years, we’ve witnessed an ever-growing list of applications unlocked by deep learning, which were made possible thanks to server-class GPUs,” said Orr Danon, CEO of Hailo. “However, there is a crucial need for an analogous architecture that replaces processors of the past, enabling deep learning to run devices at the edge. Hailo’s chip was designed from the ground up to do just that.”
Walmart adopts Artificial Intelligence software to enhance the end-to-end shopping experience. The company announced that it has acquired Aspectiva, an Israeli-based start-up from Tel aviv, for an undisclosed sum of money. Aspectiva will be joining Walmart’s Store No 8, the incubation arm launched by the retailer in 2017 to uncover the ideas that will transform the future of commerce.
Aspectiva has built an Artificial Intelligence software suite to analyzes consumer opinions and turning them into valuable insights in order to help eCommerce visitors to make informed decisions resulting in increased online conversion rates. By applying deep Natural Language Processing and Machine Learning, Aspectiva surfaces what people say about any product and understands what they feel about it. The software automatically identifies product attributes shoppers talk about by analyzing massive volumes of user generated content.
Aspectiva was founded in 2013 by Ezra Daya, Eyal Hurwitz and Yoad Arad. Prior to founding Aspectiva, The CEO Ezra Daya managed the global Text Analytics group at NICE Systems. The CTO Eyal Hurwitz is also a veteran of NICE Systems where he served as a senior scientist and led innovative initiatives, including international collaborations. Yoad Arad, VP Business Development, held senior sales roles at Clicktale, McAfee (Intel Security) and ECI.
“Aspectiva has developed incredibly sophisticated machine learning techniques and natural language processing capabilities, which are areas we believe will have profound impact on how customers will shop in the future,” said Lori Flees, Principal of Store No 8. This is not the first activity for Walmart with Israeli-based technology companies. It has also made a strategic investment in Team8, an Israeli think tank and tech incubator, launched a joint venture with Eko, an interactive media and technology company with offices in Tel Aviv and New York, and recently joined The Bridge, a technology accelerator connecting global companies with the start-up community in Israel.
Photo above: Illustration to demonstrate Protected image vs Unprotected image
The surprising start-up company from Tel Aviv, D-ID, has officially announced its initial product at TechCrunch Disrupt San Francisco 2018 this week . The company has developed a solution that protects photos and videos of organizations from face recognition, while keeping them similar to the human eye. The company serves organizations that store photos and videos of employees, customers or citizens, amog them cloud storage providers, social networks, financial institutions, health management organizations and governments that want to protect their biometric databases.
The name D-ID originated from the professional term de-identification. D-ID’s first customer is Cloudinary, an image and video management solution which helps more than 350K companies manage, optimize and deliver more than 22B media assets. The company has also signed significant agreements with customers in the financial services and automotive industry.
“Our photos contain biometric data. Using them with face recognition, anyone can track you, hack your devices and steal your identity. That’s why our photos must be protected,” says Gil Perry, CEO and Co-Founder of D-ID. “We’ve moved too fast with face recognition and it is now a threat to our fundamental human right to privacy.” The face recognition market is growing exponentially. It’s increasingly used all around the world: To analyze shopping behavior, to authenticate payments, to access smartphones and even to rank citizens’ or track people in protests.
Faces are “sensitive information”
The company’s approach to digitally manipulating images renders images unreadable by the machine learning tools that are used to identify an individual, but are imperceptible to the human eye. “We use advanced image processing and deep learning to process the photo or video in such a way that it will look similar to the human eye but machines, AI, face recognition classifiers will not be able to recognize the individual,” says Perry. He also mentioned that the solution is constantly inspected by a special “red team” that launch attacks in order to break the protection and reveal weaknesses in its defense.
The issue of image protection draws a public interest. Data privacy regulations like the European Union’s General Data Protection Regulation (GDPR), which became enforceable in May 2018, address face images as “personal sensitive information” and require companies to protects this data or risk heavy fines and lawsuits. “People are aware and concerned about the security risks of face recognition. Now is the time to protect this data and we are here to make sure it happens,” said Perry.
D-ID was founded in 2017 by CEO Gil Perry, COO Sella Blondheim and CTO Elira Kuta. The founders served in the Israeli Special Forces and intelligence unit 8200. They experienced first-hand the risks to privacy when, due to the sensitive nature of their roles, they were not allowed to share photos on social media. The company received Gartner’s Cool Vendor 2018 recognition. D-ID has raised $4mm led by Pitango Venture Capital with participation from Y Combinator, Maverick Ventures, Foundation Capital and Fenox Venture Capital.