May 23, 2019
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Tel Aviv

MultiVu raises $7M seed round for its next-gen 3D sensor

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MultiVu, a Tel Aviv-based startup that is developing a new 3D imaging solution that only relies on a single sensor and some deep learning smarts, today announced that it has raised a $7 million seed round. The round was led by crowdfunding platform OurCrowd, Cardumen Captial and Hong Kong’s Junson Capital.

Tel Aviv University’s TAU Technology Innovation Momentum Fund supported some of the earlier development of MultiVu’s core technology, which came out of Prof. David Mendlovic’s lab at the university. Mendlovic previously co-founded smartphone camera startup Corephotonics, which was recently acquired by Samsung.

The promise of MultiVu’s sensor is that it can offer 3D imaging with a single-lens camera instead of the usual two-sensor setup. This single sensor can extract depth and color data in a single shot.

This makes for a more compact setup and, by extension, a more affordable solution since it requires fewer components. All of this is powered by the company’s patented light field technology.

Currently, the team is focusing on using the sensor for face authentication in phones and other small devices. That’s obviously a growing market, but there are also plenty of other application for small 3D sensors, ranging from other security use cases to sensors for self-driving cars.

“The technology, which passed the proof-of-concept stage will bring 3D Face Authentication and affordable 3D imaging to the mobile, automotive, industrial and medical markets,” MultiVu CEO Doron Nevo said. “We are excited to be given the opportunity to commercialize this technology.”

Right now, though, the team is mostly focusing on bringing its sensor to market, though. The company will use the new funding for that, as well as new marketing and business development activities.

“We are pleased to invest in the future of 3D sensor technologies and believe that MultiVu will penetrate markets, which until now could not take advantage of costly 3D imaging solutions,” said OurCrowd Senior Investment Partner Eli Nir. “We are proud to be investing in a third company founded by Prof. David Mendlovic (who just recently sold CorePhotonics to Samsung), managed by CEO Doron Nevo – a serial entrepreneur with proven successes and a superb team they have gathered around them.”

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Hailo launches its newest deep learning chip

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Hailo, a Tel Aviv-based AI chipmaker, today announced that it is now sampling its Hailo -8 chips, the first of its deep learning processors. The new chip promises up to 26 tera operations per second (TOPS) and the company is now testing it with a number of select customers, mostly in the automotive industry.

Hailo first appeared on the radar last year, when it raised a $12.5 million Series A round. At the time, the company was still waiting for the first samples of its chips. Now, the company says that the Hailo-8 will outperform all other edge processors and do so at a smaller size and with fewer memory requirements. “By designing an architecture that relies on the core properties of neural networks, edge devices can now run deep learning applications at full scale more efficiently, effectively, and sustainably than traditional solutions, while significantly lowering costs,” the company explains.

The company also argues that its chip outperforms Nvidia’s comparable Javier Xavier AGX in some benchmarks, all while using less power and hence running cooler — something that’s especially important in small IoT devices.

We’ll have to see if that works out in practice once more engineers get their hands on these chips, of course, but there can be no doubt that the demand for AI chips on the edge continues to increase. A few years ago, after all, the market shifted away from a focus on centralizing all processing in the cloud to moving to the edge, in an effort to improve latency, reduce bandwidth cost and provide a more stable platform that doesn’t depend on network connectivity.

Like Mobileye before it (which was later acquired by Intel), Hailo is working with OEMs and tier-1 suppliers in the automotive industry to bring its chip to market, but it’s also looking at other verticals, including smart home products and really any industry where a high-performance AI chip is needed for object detection and segmentation, for example.

“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, as industries are increasingly powered and even upended by AI, 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.”

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Run.AI raises $13M for its distributed machine learning platform

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Tel Aviv’s Run.AI, a startup that is building a new virtualization and acceleration platform for deep learning, is coming out of stealth today. As a part of this announcement, the company also announced that it has now raised a total of $13 million. This includes a $3 million seed round from TLV Partners and a $10 million Series A round led by Haim Sadger’s S Capital and TLV Partners.

It’s no secret that building deep learning models take a hefty amount of GPU power or access to specialized AI chips. Run.AI argues that the virtualization layers that worked so well for in the past don’t quite cut it for training today’s AI models.

“We believe that we’re only scratching the surface of the full potential of deep learning,” Run.AI CEO and co-founder Omri Geller told me. “But the computational infrastructure needs of deep learning are a totally different ballgame. […] The rise of deep learning is triggering a new era of compute.”

Traditionally, Geller argues, virtualization was all about being generous and sharing the resources of a single machine for workloads that typically only run for a short time or use a small amount of resources. Deep learning workloads, however, are very different and are essentially selfish in that they want to take over all the available compute resources of a given machine. These training sessions also typically run for hours or days. At its core, what Run.AI offers is a new virtualization layer for distributed machine learning tasks that can across a large number of machines.

“We built a compute abstraction layer that bridges the gap between the new form of workloads and the new hardware that is evolving,” said Geller. “By using this abstraction layer, we can achieve 100x faster compute using distributed computing. We can double the resource utilization of the hardware and we can bring to companies the control over time and cost regarding deep learning.” That’s 100x faster than using a single resource, though that’s a bit aspirational as Geller also tells me that the team is seeing about a 10x speedup in production right now, though he’s confident that the team will get to 100x over time. Either way, though, the promise here is that the service will allow you to optimize the utilization of your deep learning workloads.

That’s only one part of the company’s solution, though. In addition, the company’s tools also analyze the model in order to break it down into smaller models that can then run in parallel across these servers. With that, the service can understand how many resources a workload would need and what machines to best send the given workloads to. In doing this, the system takes into account everything from available compute resources to network bandwidth, as well as the data pipeline and size.

The company also argues that this allows it to train large models that are bigger than the individual GPU memory capacity of a single machine.

There’s a financial aspect to this, too, because users can determine whether they want the service to prioritize cost savings over training speed, for example. The platform supports both private and public cloud deployments. In private clouds, cost savings are obviously less of a factor but the premise of increased utlization of the existing hardware investment will likely be a draw for many of these users.

The company, which was founded by Geller, Dr. Ronen Dar and Prof. Meir Feder, was founded in 2018. While still in stealth, it signed a number of early customers and opened a U.S. office. 

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SeeTree raises $11.5M to help farmers manage their orchards

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SeeTree, a Tel Aviv-based startup that uses drones and artificial intelligence to bring precision agriculture to their groves, today announced that it has raised an $11.5 million Series A funding round led by Hanaco Ventures, with participation from previous investors Canaan Partners Israel, Uri Levine and his investors group, iAngel and Mindset. This brings the company’s total funding to $15 million.

The idea behind the company, which also has offices in California and Brazil, is that in the past, drone-based precision agriculture hasn’t really lived up to its promise and didn’t work all that well for permanent crops like fruit trees. “In the past two decades, since the concept was born, the application of it, as well as measuring techniques, has seen limited success — especially in the permanent-crop sector,” said SeeTree CEO Israel Talpaz. “They failed to reach the full potential of precision agriculture as it is meant to be.”

He argues that the future of precision agriculture has to take a more holistic view of the entire farm. He also believes that past efforts didn’t quite offer the quality of data necessary to give permanent crop farmers the actionable recommendations they need to manage their groves.

SeeTree is obviously trying to tackle these issues and it does so by offering granular per-tree data based on the imagery gathered from drones and the company’s machine learning algorithms that then analyze this imagery. Using this data, farmers can then decide to replace trees that underperform, for example, or map out a plan to selectively harvest based on the size of a tree’s fruits and its development stages. They can then also correlate all of this data with their irrigation and fertilization infrastructure to determine the ROI of those efforts.

“Traditionally, farmers made large-scale business decisions based on intuitions that would come from limited (and often unreliable) small-scale testing done by the naked eye,” said Talpaz. “With SeeTree, farmers can now make critical decisions based on accurate and consistent small and large-scale data, connecting their actions to actual results in the field.”

SeeTree was founded by Talpaz, who like so many Israeli entrepreneurs previously worked for the country’s intelligence services, as well as Barak Hachamov (who you may remember from his early personalized news startup my6sense) and Guy Morgenstern, who has extensive experience as an R&D executive with a background in image processing and communications systems.

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Red Hat acquires hybrid cloud data management service NooBaa

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Red Hat is in the process of being acquired by IBM for a massive $34 billion, but that deal hasn’t closed yet and, in the meantime, Red Hat is still running independently and making its own acquisitions, too. As the company today announced, it has acquired Tel Aviv-based NooBaa, an early-stage startup that helps enterprises manage their data more easily and access their various data providers through a single API.

NooBaa’s technology makes it a good fit for Red Hat, which has recently emphasized its ability to help enterprise more effectively manage their hybrid and multicloud deployments. At its core, NooBaa is all about bringing together various data silos, which should make it a good fit in Red Hat’s portfolio. With OpenShift and the OpenShift Container Platform, as well as its Ceph Storage service, Red Hat already offers a range of hybrid cloud tools, after all.

“NooBaa’s technologies will augment our portfolio and strengthen our ability to meet the needs of developers in today’s hybrid and multicloud world,” writes Ranga Rangachari, the VP and general manager for storage and hyperconverged infrastructure at Red Hat, in today’s announcement. “We are thrilled to welcome a technical team of nine to the Red Hat family as we work together to further solidify Red Hat as a leading provider of open hybrid cloud technologies.”

While virtually all of Red Hat’s technology is open source, NooBaa’s code is not. The company says that it plans to open source NooBaa’s technology in due time, though the exact timeline has yet to be determined.

NooBaa was founded in 2013. The company has raised some venture funding from the likes of Jerusalem Venture Partners and OurCrowd, with a strategic investment from Akamai Capital thrown in for good measure. The company never disclosed the size of that round, though, and neither Red Hat nor NooBaa are disclosing the financial terms of the acquisition.

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