The red-hot AI hardware space gets even hotter with $56M for a startup called SambaNova Systems

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Another massive financing round for an AI chip company is coming in today, this time for SambaNova Systems — a startup founded by a pair of Stanford professors and a longtime chip company executive — to build out the next generation of hardware to supercharge AI-centric operations.

SambaNova joins an already quite large class of startups looking to attack the problem of making AI operations much more efficient and faster by rethinking the actual substrate where the computations happen. While the GPU has become increasingly popular among developers for its ability to handle the kinds of lightweight mathematics in very speedy fashion necessary for AI operations. Startups like SambaNova look to create a new platform from scratch, all the way down to the hardware, that is optimized exactly for those operations. The hope is that by doing that, it will be able to outclass a GPU in terms of speed, power usage, and even potentially the actual size of the chip. SambaNova today said it has raised a massive $ 56 million series A financing round led by GV, with participation from Redline Capital and Atlantic Bridge Ventures.

SambaNova is the product of technology from Kunle Olukotun and Chris Ré, two professors at Stanford, and led by former SVP of development Rodrigo Liang, who was also a VP at Sun for almost 8 years. When looking at the landscape, the team at SambaNova looked to work their way backwards, first identifying what operations need to happen more efficiently and then figuring out what kind of hardware needs to be in place in order to make that happen. That boils down to a lot of calculations stemming from a field of mathematics called linear algebra done very, very quickly, but it’s something that existing CPUs aren’t exactly tuned to do. And a common criticism from most of the founders in this space is that Nvidia GPUs, while much more powerful than CPUs when it comes to these operations, are still ripe for disruption.

“You’ve got these huge [computational] demands, but you have the slowing down of Moore’s law,” Olukotun said. “The question is, how do you meet these demands while Moore’s law slows. Fundamentally you have to develop computing that’s more efficient. If you look at the current approaches to improve these applications based on multiple big cores or many small, or even FPGA or GPU, we fundamentally don’t think you can get to the efficiencies you need. You need an approach that’s different in the algorithms you use and the underlying hardware that’s also required. You need a combination of the two in order to achieve the performance and flexibility levels you need in order to move forward.”

While a $ 56 million funding round for a series A might sound massive, it’s becoming a pretty standard number for startups looking to attack this space, which has an opportunity to beat massive chipmakers and create a new generation of hardware that will be omnipresent among any device that is built around artificial intelligence — whether that’s a chip sitting on an autonomous vehicle doing rapid image processing to potentially even a server within a healthcare organization training models for complex medical problems. Graphcore, another chip startup, got $ 50 million in funding from Sequoia Capital, while Cerebras Systems also received significant funding from Benchmark Capital.

Olukotun and Liang wouldn’t go into the specifics of the architecture, but they are looking to redo the operational hardware to optimize for the AI-centric frameworks that have become increasingly popular in fields like image and speech recognition. At its core, that involves a lot of rethinking of how interaction with memory occurs and what happens with heat dissipation for the hardware, among other complex problems. Apple, Google with its TPU, and reportedly Amazon have taken an intense interest in this space to design their own hardware that’s optimized for products like Siri or Alexa, which makes sense because dropping that latency to as close to zero as possible with as much accuracy in the end improves the user experience. A great user experience leads to more lock-in for those platforms, and while the larger players may end up making their own hardware, GV’s Dave Munichiello — who is joining the company’s board — says this is basically a validation that everyone else is going to need the technology soon enough.

“Large companies see a need for specialized hardware and infrastructure,” he said. “AI and large-scale data analytics are so essential to providing services the largest companies provide that they’re willing to invest in their own infrastructure, and that tells us more investment is coming. What Amazon and Google and Microsoft and Apple are doing today will be what the rest of the Fortune 100 are investing in in 5 years. I think it just creates a really interesting market and an opportunity to sell a unique product. It just means the market is really large, if you believe in your company’s technical differentiation, you welcome competition.”

There is certainly going to be a lot of competition in this area, and not just from those startups. While SambaNova wants to create a true platform, there are a lot of different interpretations of where it should go — such as whether it should be two separate pieces of hardware that handle either inference or machine training. Intel, too, is betting on an array of products, as well as a technology called Field Programmable Gate Arrays (or FPGA), which would allow for a more modular approach in building hardware specified for AI and are designed to be flexible and change over time. Both Munichiello’s and Olukotun’s arguments are that these require developers who have a special expertise of FPGA, which a sort of niche-within-a-niche that most organizations will probably not have readily available.

Nvidia has been a massive benefactor in the explosion of AI systems, but it clearly exposed a ton of interest in investing in a new breed of silicon. There’s certainly an argument for developer lock-in on Nvidia’s platforms like Cuda. But there are a lot of new frameworks, like TensorFlow, that are creating a layer of abstraction that are increasingly popular with developers. That, too represents an opportunity for both SambaNova and other startups, who can just work to plug into those popular frameworks, Olukotun said. Cerebras Systems CEO Andrew Feldman actually also addressed some of this on stage at the Goldman Sachs Technology and Internet Conference last month.

“Nvidia has spent a long time building an ecosystem around their GPUs, and for the most part, with the combination of TensorFlow, Google has killed most of its value,” Feldman said at the conference. “What TensorFlow does is, it says to researchers and AI professionals, you don’t have to get into the guts of the hardware. You can write at the upper layers and you can write in Python, you can use scripts, you don’t have to worry about what’s happening underneath. Then you can compile it very simply and directly to a CPU, TPU, GPU, to many different hardwares, including ours. If in order to do work you have to be the type of engineer that can do hand-tuned assembly or can live deep in the guts of hardware there will be no adoption… We’ll just take in their TensorFlow, we don’t have to worry about anything else.”

(As an aside, I was once told that Cuda and those other lower-level platforms are really used by AI wonks like Yann LeCun building weird AI stuff in the corners of the Internet.)

There are, also, two big question marks for SambaNova: first, it’s very new, having started in just November while many of these efforts for both startups and larger companies have been years in the making. Munichiello’s answer to this is that the development for those technologies did, indeed, begin a while ago — and that’s not a terrible thing as SambaNova just gets started in the current generation of AI needs. And the second, among some in the valley, is that most of the industry just might not need hardware that’s does these operations in a blazing fast manner. The latter, you might argue, could just be alleviated by the fact that so many of these companies are getting so much funding, with some already reaching close to billion-dollar valuations.

But, in the end, you can now add SambaNova to the list of AI startups that have raised enormous rounds of funding — one that stretches out to include a myriad of companies around the world like Graphcore and Cerebras Systems, as well as a lot of reported activity out of China with companies like Cambricon Technology and Horizon Robotics. This effort does, indeed, require significant investment not only because it’s hardware at its base, but it has to actually convince customers to deploy that hardware and start tapping the platforms it creates, which supporting existing frameworks hopefully alleviates.

“The challenge you see is that the industry, over the last ten years, has underinvested in semiconductor design,” Liang said. “If you look at the innovations at the startup level all the way through big companies, we really haven’t pushed the envelope on semiconductor design. It was very expensive and the returns were not quite as good. Here we are, suddenly you have a need for semiconductor design, and to do low-power design requires a different skillset. If you look at this transition to intelligent software, it’s one of the biggest transitions we’ve seen in this industry in a long time. You’re not accelerating old software, you want to create that platform that’s flexible enough [to optimize these operations] — and you want to think about all the pieces. It’s not just about machine learning.”

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BP Just Invested $5 Million in a Company That Builds EV Charging Systems

Slow Transition

BP, one of the world’s largest oil companies, is slowly inching their way into the clean energy business. The company announced on Monday, January 30, that they are investing $ 5 million in FreeWire Technologies, a U.S. manufacturer of rapid charging systems for electric vehicles (EVs).

In the next couple of months, BP will add FreeWire’s EV charging systems to various gas stations in the U.K. These system will be capable of fully charging an EV in 30 minutes.

An EV being charged by a BP branded charging station. Image Credit: BP
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While BP didn’t disclose just how many charging systems it will implement, the company did tell The Guardian they plan to expand to other European locations after March.

“Mobility is changing, and BP is committed to remaining the fuel retailer of choice into the future,” said Tufan Erginbilgic, chief executive officer at BP’s Downstream division, in a press release. “EV charging will undoubtedly become an important part of our business, but customer demand and the technologies available are still evolving.”

Displaying Interest

BP isn’t the first oil company to invest in EV charging stations. In October, Shell announced they were acquiring NewMotion, another company that specializes in building public EV charging stations. Soon after, they began adding the charging stations to several gas stations in the U.K. with plans to expand to other locations in Europe.

“When you add this customer offer to our current roll out of fast charging points on Shell forecourts, we believe we are developing the full raft of charge solutions required to support the future of EVs,” Matthew Tipper, Shell VP for New Fuels, said in a press release at the time of the announcement.

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While Shell and BP still primarily push fossil fuels, they’re clearly both keeping an eye on clean energy and making strategic investments and partnerships.

In November, Shell teamed up with IONITY, a partnership between BMW, Ford, Volkswagen, and other automakers to build a High-Power Charging (HPC) network in Europe. The deal will see IONITY’s 350-kilowatt charging points placed at Shell stations throughout Europe, including ones in Belgium, France, the Netherlands, Austria, the Czech Republic, Hungary, Poland, Slovakia, Slovenia, and the U.K.

In December, BP formed a strategic partnership with Lightsource, Europe’s largest solar development company. In exchange for a stake in the company, BP agreed to invest $ 200 million into Lightsource’s various solar projects across the globe.

BP and Shell aren’t the oil companies investing in fossil fuel alternatives, either.

Exxon Mobil is investing $ 1 billion per year in clean energy research. As part of the endeavor, they are collaborating with small companies and universities on a number of projects, including one focused on turning algae into a biofuel that could replace diesel.

In September, A.P. Moller-Maersk, reportedly the largest company in Denmark, sold their last remaining oil firm for $ 7.45 billion. The company intends to invest the revenue from the sale into clean energy, and Denmark has already proven it can sustain itself with wind energy alone.

The transition from fossil fuels to clean energy may not be happening as quickly as we’d like, but it is happening. We’re in the midst of a clean energy revolution, and with renewable energy prices expected to drop below those of fossil fuels by 2020, it’s obvious that fossil fuel’s days are numbered.

The post BP Just Invested $ 5 Million in a Company That Builds EV Charging Systems appeared first on Futurism.


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Australia sets regulations for driverless vehicle systems

Road traffic authorities in Australia have received the regulations they must comply with to roll out intelligent transport systems (ITS)

ITS support driverless vehicles by enabling vehicle-to-vehicle, vehicle-to-person, and vehicle-to-infrastructure communications. Today’s regulations mark a key milestone towards mass rollout of driverless vehicles in Australia.

"ITS are expected to make roads smarter, safer, and cleaner through the use of communications technologies," says ACMA acting chair James Cameron. "The new Class Licence will facilitate the rollout of the latest transportation communications technology, putting Australia on par with other nations adopting ITS."

The 5.9GHz band has been made available for ITS usage in Australia as part of the Radiocommunications (Intelligent Transport Systems) Class Licence 2017 regulations.

An ITS station can be operated by a party with a Class License on the condition that it’s operated on a frequency, or within a range of frequencies, greater than 5855 MHz and not greater than 5925 MHz.

The power output must not exceed a maximum EIRP of 23 dBm/MHz and it cannot be operated within 70kms of the Murchison Radioastronomy Observatory. The station must also comply with ETSI Standard EN 302 571.

A key goal of the new regulations is to bring Australia in line with other major vehicle markets such as the United States and European Union. This regulatory alignment will aid with research and development, and the eventual rollout of driverless vehicles.

"Harmonising Australia's ITS arrangements with wider global developments means Australian motorists are more likely to enjoy the benefits of connected vehicles as they become available," ACMA said in a statement.

What are your thoughts on Australia’s new driverless vehicle regulations? Let us know in the comments. Latest from the homepage