edge computing
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Deeplite raises $6M seed to deploy ML on edge with fewer compute resources
One of the issues with deploying a machine learning application is that it tends to be expensive and highly compute intensive. Deeplite, a startup based in Montreal, wants to change that by providing a way to reduce the overall size of the model, allowing it to run on hardware with far fewer resources.
Today, the company announced a $6 million seed investment. Boston-based venture capital firm PJC led the round, with help from Innospark Ventures, Differential Ventures and Smart Global Holdings. Somel Investments, BDC Capital and Desjardins Capital also participated.
Nick Romano, CEO and co-founder at Deeplite, says the company aims to take complex deep neural networks that require a lot of compute power to run, tend to use up a lot of memory and can consume batteries at a rapid pace, and help them run more efficiently with fewer resources.
“Our platform can be used to transform those models into a new form factor to be able to deploy it into constrained hardware at the edge,” Romano explained. Those devices could be as small as a cell phone, a drone or even a Raspberry Pi, meaning that developers could deploy AI in ways that just wouldn’t be possible in most cases right now.
The company has created a product called Neutrino that lets you specify how you want to deploy your model and how much you can compress it to reduce the overall size and the resources required to run it in production. The idea is to run a machine learning application on an extremely small footprint.
Davis Sawyer, chief product officer and co-founder, says that the company’s solution comes into play after the model has been built, trained and is ready for production. Users supply the model and the data set and then they can decide how to build a smaller model. That could involve reducing the accuracy a bit if there is a tolerance for that, but chiefly it involves selecting a level of compression — how much smaller you can make the model.
“Compression reduces the size of the model so that you can deploy it on a much cheaper processor. We’re talking in some cases going from 200 megabytes down to on 11 megabytes or from 50 megabytes to 100 kilobytes,” Davis explained.
Rob May, who is leading the investment for PJC, says that he was impressed with the team and the technology the startup is trying to build.
“Deploying AI, particularly deep learning, on resource-constrained devices, is a broad challenge in the industry with scarce AI talent and know-how available. Deeplite’s automated software solution will create significant economic benefit as Edge AI continues to grow as a major computing paradigm,” May said in a statement.
The idea for the company has roots in the TandemLaunch incubator in Montreal. It launched officially as a company in mid-2019 and today has 15 employees, with plans to double that by the end of this year. As it builds the company, Romano says the founders are focused on building a diverse and inclusive organization.
“We’ve got a strategy that’s going to find us the right people, but do it in a way that is absolutely diverse and inclusive. That’s all part of the DNA of the organization,” he said.
When it’s possible to return to work, the plan is to have offices in Montreal and Toronto that act as hubs for employees, but there won’t be any requirement to come into the office.
“We’ve already discussed that the general approach is going to be that people can come and go as they please, and we don’t think we will need as large an office footprint as we may have had in the past. People will have the option to work remotely and virtually as they see fit,” Romano said.
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F5 snags Volterra multi-cloud management startup for $500M
Applications networking company F5 announced today that it is acquiring Volterra, a multi-cloud management startup, for $500 million. That breaks down to $440 million in cash and $60 million in deferred and unvested incentive compensation.
Volterra emerged in 2019 with a $50 million investment from multiple sources, including Khosla Ventures and Mayfield, along with strategic investors like M12 (Microsoft’s venture arm) and Samsung Ventures. As the company described it to me at the time of the funding:
Volterra has innovated a consistent, cloud-native environment that can be deployed across multiple public clouds and edge sites — a distributed cloud platform. Within this SaaS-based offering, Volterra integrates a broad range of services that have normally been siloed across many point products and network or cloud providers.
The solution is designed to provide a single way to view security, operations and management components.
F5 president and CEO François Locoh-Donou sees Volterra’s edge solution integrating across its product line. “With Volterra, we advance our Adaptive Applications vision with an Edge 2.0 platform that solves the complex multi-cloud reality enterprise customers confront. Our platform will create a SaaS solution that solves our customers’ biggest pain points,” he said in a statement.
Volterra founder and CEO Ankur Singla, writing in a company blog post announcing the deal, says the need for this solution only accelerated during 2020 when companies were shifting rapidly to the cloud due to the pandemic. “When we started Volterra, multi-cloud and edge were still buzzwords and venture funding was still searching for tangible use cases. Fast forward three years and COVID-19 has dramatically changed the landscape — it has accelerated digitization of physical experiences and moved more of our day-to-day activities online. This is causing massive spikes in global Internet traffic while creating new attack vectors that impact the security and availability of our increasing set of daily apps,” he wrote.
He sees Volterra’s capabilities fitting in well with the F5 family of products to help solve these issues. While F5 had a quiet 2020 on the M&A front, today’s purchase comes on top of a couple of major acquisitions in 2019, including Shape Security for $1 billion and NGINX for $670 million.
The deal has been approved by both companies’ boards, and is expected to close before the end of March, subject to regulatory approvals.
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Edge computing startup Edgify secures $6.5M seed from Octopus, Mangrove and semiconductor
Edgify, which builds AI for edge computing, has secured a $6.5 million seed funding round backed by Octopus Ventures, Mangrove Capital Partners and an unnamed semiconductor giant. The name was not released but TechCrunch understands it may be Intel Corp. or Qualcomm Inc.
Edgify’s technology allows “edge devices” (devices at the edge of the internet) to interpret vast amounts of data, train an AI model locally and then share that learning across its network of similar devices. This then trains all the other devices in anything from computer vision, NLP, voice recognition or any other form of AI.
The technology can be applied to anything from MRI machines, connected cars, checkout lanes, mobile devices and anything that has a CPU, GPU or NPU. Edgify’s technology is already being used in supermarkets, for instance.
Ofri Ben-Porat, CEO and co-founder of Edgify, commented in a statement: “Edgify allows companies, from any industry, to train complete deep learning and machine learning models, directly on their own edge devices. This mitigates the need for any data transfer to the Cloud and also grants them close to perfect accuracy every time, and without the need to retrain centrally.”
Mangrove partner Hans-Jürgen Schmitz, who will join Edgify’s Board comments: “We expect a surge in AI adoption across multiple industries with significant long-term potential for Edgify in medical and manufacturing, just to name a few.”
Simon King, partner and Deep Tech Investor at Octopus Ventures added: “As the interconnected world we live in produces more and more data, AI at the edge is becoming increasingly important to process large volumes of information.”
So-called “edge computing” is seen as being one of the forefronts of deep tech right now.
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Latent AI makes edge AI workloads more efficient
Latent AI, a startup that was spun out of SRI International, makes it easier to run AI workloads at the edge by dynamically managing workloads as necessary.
Using its proprietary compression and compilation process, Latent AI promises to compress library files by 10x and run them with 5x lower latency than other systems, all while using less power thanks to its new adaptive AI technology, which the company is launching as part of its appearance in the TechCrunch Disrupt Battlefield competition today.
Founded by CEO Jags Kandasamy and CTO Sek Chai, the company has already raised a $6.5 million seed round led by Steve Jurvetson of Future Ventures and followed by Autotech Ventures .
Before starting Latent AI, Kandasamy sold his previous startup OtoSense to Analog Devices (in addition to managing HPE Mid-Market Security business before that). OtoSense used data from sound and vibration sensors for predictive maintenance use cases. Before its sale, the company worked with the likes of Delta Airlines and Airbus.
Image Credits: Latent AI
In some ways, Latent AI picks up some of this work and marries it with IP from SRI International .
“With OtoSense, I had already done some edge work,” Kandasamy said. “We had moved the audio recognition part out of the cloud. We did the learning in the cloud, but the recognition was done in the edge device and we had to convert quickly and get it down. Our bill in the first few months made us move that way. You couldn’t be streaming data over LTE or 3G for too long.”
At SRI, Chai worked on a project that looked at how to best manage power for flying objects where, if you have a single source of power, the system could intelligently allocate resources for either powering the flight or running the onboard compute workloads, mostly for surveillance, and then switch between them as needed. Most of the time, in a surveillance use case, nothing happens. And while that’s the case, you don’t need to compute every frame you see.
“We took that and we made it into a tool and a platform so that you can apply it to all sorts of use cases, from voice to vision to segmentation to time series stuff,” Kandasamy explained.
What’s important to note here is that the company offers the various components of what it calls the Latent AI Efficient Inference Platform (LEIP) as standalone modules or as a fully integrated system. The compressor and compiler are the first two of these and what the company is launching today is LEIP Adapt, the part of the system that manages the dynamic AI workloads Kandasamy described above.
Image Credits: Latent AI
In practical terms, the use case for LEIP Adapt is that your battery-powered smart doorbell, for example, can run in a low-powered mode for a long time, waiting for something to happen. Then, when somebody arrives at your door, the camera wakes up to run a larger model — maybe even on the doorbell’s base station that is plugged into power — to do image recognition. And if a whole group of people arrives at ones (which isn’t likely right now, but maybe next year, after the pandemic is under control), the system can offload the workload to the cloud as needed.
Kandasamy tells me that the interest in the technology has been “tremendous.” Given his previous experience and the network of SRI International, it’s maybe no surprise that Latent AI is getting a lot of interest from the automotive industry, but Kandasamy also noted that the company is working with consumer companies, including a camera and a hearing aid maker.
The company is also working with a major telco company that is looking at Latent AI as part of its AI orchestration platform and a large CDN provider to help them run AI workloads on a JavaScript backend.
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SiMa.ai announces $30M Series A to build out lower-power edge chip solution
Krishna Rangasayee, founder and CEO, at SiMa.ai, has 30 years of experience in the semiconductor industry. He decided to put that experience to work in a startup and launched SiMa.ai last year with the goal of building an ultra low-power software and chip solution for machine learning at the edge.
Today he announced a $30 million Series A led by Dell Technologies Capital with help from Amplify Partners, Wing Venture Capital and +ND Capital. Today’s investment brings the total raised to $40 million, according to the company.
Rangasayee says in his years as a chip executive he saw a gap in the machine learning market for embedded devices running at the edge and he decided to start the company to solve that issue.
“While the majority of the market was serviced by traditional computing, machine learning was beginning to make an impact and it was really amazing. I wanted to build a company that would bring machine learning at significant scale to help the problems with embedded markets,” he told TechCrunch.
The company is trying to focus on efficiency, which it says will make the solution more environmentally friendly by using less power. “Our solution can scale high performance at the lowest power efficiency, and that translates to the highest frames per second per watt. We have built out an architecture and a software solution that is at a minimum 30x better than anybody else on the frames per second,” he explained.
He added that achieving that efficiency required them to build a chip from scratch because there isn’t a solution available off the shelf today that could achieve that.
So far the company has attracted 20 early design partners, who are testing what they’ve built. He hopes to have the chip designed and the software solution in Beta in the Q4 timeframe this year, and is shooting for chip production by Q2 in 2021.
He recognizes that it’s hard to raise this kind of money in the current environment and he’s grateful to the investors, and the design partners who believe in his vision. The timing could actually work in the company’s favor because it can hunker down and build product while navigating through the current economic malaise.
Perhaps by 2021 when the product is in production, the market and the economy will be in better shape and the company will be ready to deliver.
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AWS is sick of waiting for your company to move to the cloud
AWS held its annual re:Invent customer conference last week in Las Vegas. Being Vegas, there was pageantry aplenty, of course, but this year’s model felt a bit different than in years past, lacking the onslaught of major announcements we are used to getting at this event.
Perhaps the pace of innovation could finally be slowing, but the company still had a few messages for attendees. For starters, AWS CEO Andy Jassy made it clear he’s tired of the slow pace of change inside the enterprise. In Jassy’s view, the time for incremental change is over, and it’s time to start moving to the cloud faster.
AWS also placed a couple of big bets this year in Vegas to help make that happen. The first involves AI and machine learning. The second, moving computing to the edge, closer to the business than the traditional cloud allows.
The question is what is driving these strategies? AWS had a clear head start in the cloud, and owns a third of the market, more than double its closest rival, Microsoft. The good news is that the market is still growing and will continue to do so for the foreseeable future. The bad news for AWS is that it can probably see Google and Microsoft beginning to resonate with more customers, and it’s looking for new ways to get a piece of the untapped part of the market to choose AWS.
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Edge computing startup Pensando comes out of stealth mode with a total of $278 million in funding
Pensando, an edge computing startup founded by former Cisco engineers, came out of stealth mode today with an announcement that it has raised a $145 million Series C. The company’s software and hardware technology, created to give data centers more of the flexibility of cloud computing servers, is being positioned as a competitor to Amazon Web Services Nitro.
The round was led by Hewlett Packard Enterprise and Lightspeed Venture Partners and brings Pensando’s total raised so far to $278 million. HPE chief technology officer Mark Potter and Lightspeed Venture partner Barry Eggers will join Pensando’s board of directors. The company’s chairman is former Cisco CEO John Chambers, who is also one of Pensando’s investors through JC2 Ventures.
Pensando was founded in 2017 by Mario Mazzola, Prem Jain, Luca Cafiero and Soni Jiandani, a team of engineers who spearheaded the development of several of Cisco’s key technologies, and founded four startups that were acquired by Cisco, including Insieme Networks. (In an interview with Reuters, Pensando chief financial officer Randy Pond, a former Cisco executive vice president, said it isn’t clear if Cisco is interested in acquiring the startup, adding “our aspirations at this point would be to IPO. But, you know, there’s always other possibilities for monetization events.”)
The startup claims its edge computing platform performs five to nine times better than AWS Nitro, in terms of productivity and scale. Pensando prepares data center infrastructure for edge computing, better equipping them to handle data from 5G, artificial intelligence and Internet of Things applications. While in stealth mode, Pensando acquired customers including HPE, Goldman Sachs, NetApp and Equinix.
In a press statement, Potter said “Today’s rapidly transforming, hyper-connected world requires enterprises to operate with even greater flexibility and choices than ever before. HPE’s expanding relationship with Pensando Systems stems from our shared understanding of enterprises and the cloud. We are proud to announce our investment and solution partnership with Pensando and will continue to drive solutions that anticipate our customers’ needs together.”
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Satya Nadella looks to the future with edge computing
Speaking today at the Microsoft Government Leaders Summit in Washington, DC, Microsoft CEO Satya Nadella made the case for edge computing, even while pushing the Azure cloud as what he called “the world’s computer.”
While Amazon, Google and other competitors may have something to say about that, marketing hype aside, many companies are still in the midst of transitioning to the cloud. Nadella says the future of computing could actually be at the edge, where computing is done locally before data is then transferred to the cloud for AI and machine learning purposes. What goes around, comes around.
But as Nadella sees it, this is not going to be about either edge or cloud. It’s going to be the two technologies working in tandem. “Now, all this is being driven by this new tech paradigm that we describe as the intelligent cloud and the intelligent edge,” he said today.
He said that to truly understand the impact the edge is going to have on computing, you have to look at research, which predicts there will be 50 billion connected devices in the world by 2030, a number even he finds astonishing. “I mean this is pretty stunning. We think about a billion Windows machines or a couple of billion smartphones. This is 50 billion [devices], and that’s the scope,” he said.
The key here is that these 50 billion devices, whether you call them edge devices or the Internet of Things, will be generating tons of data. That means you will have to develop entirely new ways of thinking about how all this flows together. “The capacity at the edge, that ubiquity is going to be transformative in how we think about computation in any business process of ours,” he said. As we generate ever-increasing amounts of data, whether we are talking about public sector kinds of use case, or any business need, it’s going to be the fuel for artificial intelligence, and he sees the sheer amount of that data driving new AI use cases.
“Of course when you have that rich computational fabric, one of the things that you can do is create this new asset, which is data and AI. There is not going to be a single application, a single experience that you are going to build, that is not going to be driven by AI, and that means you have to really have the ability to reason over large amounts of data to create that AI,” he said.
Nadella would be more than happy to have his audience take care of all that using Microsoft products, whether Azure compute, database, AI tools or edge computers like the Data Box Edge it introduced in 2018. While Nadella is probably right about the future of computing, all of this could apply to any cloud, not just Microsoft.
As computing shifts to the edge, it’s going to have a profound impact on the way we think about technology in general, but it’s probably not going to involve being tied to a single vendor, regardless of how comprehensive their offerings may be.
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Data storage company Cloudian launches a new edge analytics subsidiary called Edgematrix
Cloudian, a company that enables businesses to store and manage massive amounts of data, announced today the launch of Edgematrix, a new unit focused on edge analytics for large data sets. Edgematrix, a majority-owned subsidiary of Cloudian, will first be available in Japan, where both companies are based. It has raised a $9 million Series A from strategic investors NTT Docomo, Shimizu Corporation and Japan Post Capital, as well as Cloudian co-founder and CEO Michael Tso and board director Jonathan Epstein. The funding will be used on product development, deployment and sales and marketing.
Cloudian itself has raised a total of $174 million, including a $94 million Series E round announced last year. Its products include the Hyperstore platform, which allows businesses to store hundreds of petrabytes of data on premise, and software for data analytics and machine learning. Edgematrix uses Hyperstore for storing large-scale data sets and its own AI software and hardware for data processing at the “edge” of networks, closer to where data is collected from IoT devices like sensors.
The company’s solutions were created for situations where real-time analytics is necessary. For example, it can be used to detect the make, model and year of cars on highways so targeted billboard ads can be displayed to their drivers.
Tso told TechCrunch in an email that Edgematrix was launched after Cloudian co-founder and president Hiroshi Ohta and a team spent two years working on technology to help Cloudian customers process and analyze their data more efficiently.
“With more and more data being created at the edge, including IoT data, there’s a growing need for being able to apply real-time data analysis and decision-making at or near the edge, minimizing the transmission costs and latencies involved in moving the data elsewhere,” said Tso. “Based on the initial success of a small Cloudian team developing AI software solutions and attracting a number of top-tier customers, we decided that the best way to build on this success was establishing a subsidiary with strategic investors.”
Edgematrix is launching in Japan first because spending on AI systems there is expected to grow faster than in any other market, at a compound annual growth rate of 45.3% from 2018 to 2023, according to IDC.
“Japan has been ahead of the curve as an early adopter of AI technology, with both the governmetn and private sector viewing it as essential to boosting productivity,” said Tso. “Edgematrix will focus on the Japanese market for at least the next year, and assuming that all goes well, it would then expand to North America and Europe.”
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Microsoft brings Azure SQL Database to the edge (and Arm)
Microsoft today announced an interesting update to its database lineup with the preview of Azure SQL Database Edge, a new tool that brings the same database engine that powers Azure SQL Database in the cloud to edge computing devices, including, for the first time, Arm-based machines.
Azure SQL Edge, Azure corporate vice president Julia White writes in today’s announcement, “brings to the edge the same performant, secure and easy to manage SQL engine that our customers love in Azure SQL Database and SQL Server.”
The new service, which will also run on x64-based devices and edge gateways, promises to bring low-latency analytics to edge devices as it allows users to work with streaming data and time-series data, combined with the built-in machine learning capabilities of Azure SQL Database. Like its larger brethren, Azure SQL Database Edge will also support graph data and comes with the same security and encryption features that can, for example, protect the data at rest and in motion, something that’s especially important for an edge device.
As White rightly notes, this also ensures that developers only have to write an application once and then deploy it to platforms that feature Azure SQL Database, good old SQL Server on premises and this new edge version.
SQL Database Edge can run in both connected and fully disconnected fashion, something that’s also important for many use cases where connectivity isn’t always a given, yet where users need the kind of data analytics capabilities to keep their businesses (or drilling platforms, or cruise ships) running.
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