Mythic nets $40M to create a new breed of efficient AI-focused hardware

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Another huge financing round is coming in for an AI company today, this time for a startup called Mythic getting a fresh $ 40 million as it appears massive deals are closing left and right in the sector.

Mythic particularly focuses on the inference side of AI operations — basically making the calculation on the spot for something based off an extensively-trained model. The chips are designed to be low power, small, and achieve the same kind of performance you’d expect from a GPU in terms of the lightning-fast operations that algorithms need to perform to figure out whether or not that thing your car is about to run into is a cat or just some text on the road. SoftBank Ventures led this most-recent round of funding, with a strategic investment also coming from Lockheed Martin Ventures. ARM executive Rene Haas will also be joining the company’s board of directors.

“The key to getting really high performance and really good energy efficiency is to keep everything on the chip,” Henry said. “The minute you have to go outside the chip to memory, you lose all performance and energy. It just goes out the window. Knowing that, we found that you can actually leverage flash memory in a very special way. The limit there is, it’s for inference only, but we’re only going after the inference market — it’s gonna be huge. On top of that, the challenge is getting the processors and memory as close together as possible so you don’t have to move around the data on the chip.”

Mythic, like other startups, is looking to ease the back-and-forth trips to memory on the processors in order to speed things up and lower the power consumption, and CEO Michael Henry says the company has figured out how to essentially do the operations — based in a field of mathematics called linear algebra — on flash memory itself.

Mythic’s approach is designed to be what Henry calls more analog. To visualize how it might work, imagine a set-up in Minecraft, with a number of different strings of blocks leading to an end gate. If you flipped a switch to turn 50 of those strings on with some unit value, leaving the rest off, and joined them at the end and saw the combined final result of the power, you would have completed something similar to an addition operation leading to a sum of 50 units. Mythic’s chips are designed to do something not so dissimilar, finding ways to complete those kinds of analog operations for addition and multiplication in order to handle the computational requirements for an inference operation. The end result, Henry says, consumes less power and dissipates less heat while still getting just enough accuracy to get the right solution (more technically: the calculations are 8-bit results).

After that, the challenge is sticking a layer on top of that to make it look and behave like a normal chip to a developer. The goal is to, like other players in the AI hardware space, just plug into frameworks like TensorFlow. Those frameworks abstract out all the complicated tooling and tuning required for such a specific piece of hardware and make it very approachable and easy for developers to start building machine learning projects. Andrew Feldman, CEO of another AI hardware startup called Cerebras Systems, said at the Goldman Sachs Technology and Internet conference last month that frameworks like TensorFlow had  most of the value Nvidia had building up an ecosystem for developers on its own system.

Henry, too, is a big TensorFlow fan. And for good reason: it’s because of frameworks like TensorFlow that allow next-generation chip ideas to even get off the ground in the first place. These kinds of frameworks, which have become increasingly popular with developers, have abstracted out the complexity of working with specific low-level hardware like a field programmable gate array (FPGA) or a GPU. That’s made building machine learning-based operations much easier for developers and led to an explosion of activity when it comes to machine learning, whether it’s speech or image recognition among a number of other use cases.

“Things like TensorFlow make our lives so much easier,” Henry said. “Once you have a neural network described on TensorFlow, it’s on us to take that and translate that onto our chip. We can abstract that difficulty by having an automatic compiler.”

While many of these companies are talking about getting massive performance gains over a GPU — and, to be sure, Henry hopes that’ll be the case — the near term goal for Mythic is to match the performance of a $ 1,000 GPU while showing it can take up less space and consume less power. There’s a market for the card that customers can hot swap in right away. Henry says the company is focused on using a PCI-E interface, a very common plug-and-play system, and that’s it.

The challenge for Mythic, however, is going to get into the actual design of some of the hardware that comes out. It’s one thing to sell a bunch of cards that companies can stick into their existing hardware, but it’s another to get embedded into the actual pieces of hardware themselves — which is what’s going to need to happen if it wants to be a true workhorse for devices on the edge, like security cameras or things handling speech recognition. That makes the buying cycle a little more difficult, but at the same time, there will be billions of devices out there that need advanced hardware to power their inference operations.

“If we can sell a PCI card, you buy it and drop it in right away, but those are usually for low-volume, high-selling price products,” Henry said. “The other customers we serve design you into the hardware products. That’s a longer cycle, that can take upwards of a year. For that, typically the volumes are much higher. The nice thing is that you’re really really sticky. If they design you into a product you’re really sticky. We can go after both, we can go after board sales, and then go after design.”

There are probably going to be two big walls to Mythic, much less any of the other players out there. The first is that none of these companies have shipped a product. While Mythic, or other companies, might have a proof-of-concept chip that can drop on the table, getting a production-ready piece of next-generation silicon is a dramatic undertaking. Then there’s the process of not only getting people to buy the hardware, but actually convincing them that they’ll have the systems in place to ensure that developers will build on that hardware. Mythic says it plans to have a sample for customers by the end of the year, with a production product by 2019.

That also explains why Mythic, along with those other startups, are able to raise enormous rounds of money — which means there’s going to be a lot of competition amongst all of them. Here’s a quick list of what fundraising has happened so far: SambaNova Systems raised $ 56 million last week; Graphcore raised $ 50 million in November last year; Cerebras Systems’s first round was $ 25 million in December 2016; and this isn’t even counting an increasing amount of activity happening among companies in China. There’s still definitely a segment of investors that consider the space way too hot (and there is, indeed, a ton of funding) or potentially unnecessary if you don’t need the bleeding edge efficiency or power of these products.

And there are, of course, the elephants in the room in the form of Nvidia and to a lesser extent Intel. The latter is betting big on FPGA and other products, while Nvidia has snapped up most of the market thanks to GPUs being much more efficient at the kind of math needed for AI. The play for all these startups is they can be faster, more efficient, or in the case of Mythic, cheaper than all those other options. It remains to be seen whether they’ll unseat Nvidia, but nonetheless there’s an enormous amount of funding flowing in.

“The question is, is someone going to be able to beat Nvidia when they have the valuation and cash reserves,” Henry said. “But the thing, is we’re in a different market. We’re going after the edge, we’re going after things embedded inside phones and cars and drones and robotics, for applications like AR and VR, and it’s just really a different market. When investors analyze us they have to think of us differently. They don’t think, is this the one that wins Nvidia, they think, are one or more of these powder keg markets explode. It’s a different conversation for us because we’re an edge company.”

Mobile – TechCrunch

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Steve Jobs signed employment application nets over 3x estimated pre-auction value of $50,000

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We reported last month that several rare items signed by Steve Jobs would be going up for auction. Now, the auctions have been completed and one of the most sought after items went for over $ 170,000, three times its estimated value.

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Zoolz Cloud Storage nets you 2TB of cloud backup for life—and it’s on sale for less than $50 [Sponsored Deal]

How Complete Beginners are using an ‘Untapped’ Google Network to create Passive Income ON DEMAND

Note: The following post was written by our sponsor, StackSocial.

When you think of cloud storage, Dropbox and OneDrive are probably the first of a host of services that come to mind. Next, are the exorbitant rates these solutions charge to handle your information, while not giving you much flexibility with the space you’re given. That said, it’s no wonder why many prefer hard storage options off the cloud or just stick to the free cloud plans that give out a few gigabytes here and there but can’t possibly back up all of our sensitive files.

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Zoolz Cloud Storage nets you 2TB of cloud backup for life—and it’s on sale for less than $ 50 [Sponsored Deal] was written by the awesome team at Android Police.

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MIT’s new chip could bring neural nets to battery-powered gadgets

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 MIT researchers have developed a chip designed to speed up the hard work of running neural networks, while also reducing the power consumed when doing so dramatically – by up to 95 percent, in fact. The basic concept involves simplifying the chip design so that shuttling of data between different processors on the same chip is taken out of the equation. The big advantage of this new… Read More
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Alibaba was selling counterfeit Brooklyn Nets gear even as its co-founder was buying a stake in the team

Fake Nets apparel was readily accessible on the Taobao shopping site.

On the day news broke that Alibaba co-founder Joe Tsai was purchasing 49 percent of the Brooklyn Nets NBA franchise, plenty of the team’s gear was available on his company’s shopping sites.

The problem is that many of those items were counterfeit, Recode found after reviewing listings on Alibaba’s Taobao shopping site on that October day as well as on Singles Day, China’s massive online shopping holiday on Nov. 11.

Some seemed like obvious fakes just by looking at them.

A counterfeit purple Brooklyn Nets sweatshirt listed on Alibaba’s Taobao shopping site.
A counterfeit Brooklyn Nets sweatshirt listed on Alibaba’s Taobao shopping site. 178 Chinese Yuan is approximately $ 27.

While in other cases, a sale price of $ 7 for a basketball jersey coupled with blurred-out labels gave it away.

A counterfeit Jeremy Lin Brooklyn Nets jersey listed for sale on Alibaba’s Taobao shopping site.
A counterfeit Jeremy Lin Brooklyn Nets jersey listed on Alibaba’s Taobao shopping site.

In a statement, an Alibaba spokeswoman said: “Once these listings, which were posted by third-party sellers on Alibaba’s marketplace, were brought to our attention, we took action to investigate and remove them. As is our commitment and practice, we will continue to do so in close cooperation with rights holders.”

For years, Alibaba has struggled to fight the perception that its shopping sites — particularly Taobao, where millions of small merchants hawk their wares across one billion listings — were hospitable to counterfeit and knock-off goods.

In December, the U.S. placed Taobao back on its “Notorious Markets List” of “online and physical marketplaces that reportedly engage in and facilitate substantial copyright piracy and trademark counterfeiting.”

At the time, Alibaba said it was “very disappointed” with the decision and insinuated it was “influenced by the current political climate” following the election of President Donald Trump, who has criticized the current trade relationship between the two nations.

Earlier this year, the company announced the “Big Data Anti-Counterfeiting Alliance” with big consumer brands to share information and data to curb counterfeiting.

Alibaba says it uses machine learning and other advanced technologies to identify and weed out fakes. It said it closed down 180,000 merchant storefronts on its properties. The company also relies on rights-holders — or brands — to help it identify fakes when its technology doesn’t.

A solution still seems a ways off, though. Even after the Nets listings in question were taken down, other NBA team jerseys that appear to be counterfeits still remain.

An NBA spokesperson did not respond to a request for comment.


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Researchers have found a way to use neural networks to create DSLR-quality photos from snapshots taken with low-end smartphones. A team of scientists at the ETH Zurich Computer Vision Lab recently published a paper describing a deep learning approach that uses neural networks to translate photos taken by cameras with limited capabilities into DSLR-quality photos automatically. “We tackle this problem by introducing a weakly supervised photo enhancer — a novel image-to-image GAN-based architecture,” they wrote.
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iFixit’s Essential Phone teardown reveals a very tedious repair process, nets it a score of 1

It’s hard to deny that the Essential Phone has some good-looking hardware. Its titanium frame and ceramic back might make the phone a bit more durable, but what happens when you do break the phone? iFixit has just torn down Andy Rubin’s new phone, and the results are not good.

The teardown begins by applying some “Super Cold” aerosol to make the process easier. Some heat is then applied, and sharp tools are used to get the phone’s ceramic back detached from its large amount of adhesive.

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Nvidia Embraces Deep Neural Nets With Volta

At this year’s GPU Technology Conference, Nvidia’s premier conference for technical computing with graphic processors, the company reserved the top keynote for its CEO Jensen Huang. Over the years, the GTC conference went from a segment in a larger, mostly gaming-oriented and somewhat scattershot conference called “nVision” to become one of the key conferences that mixes academic and commercial high-performance computing. Jensen’s message was that GPU-accelerated machine learning is growing to touch every aspect of computing.
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IIoT Security Company Mocana Nets $11M Series F

Mocana, a software and services provider of IoT security solutions for industrial control systems, embedded systems, and the Internet of Things raised $ 11 million Series F on May 16, 2017. The new funding round was led by Sway Ventures, Shasta Ventures, Trident Capital Fund and GE Ventures.

The company which started out in 2004 and boasts more than 200 industrial automation and IoT customers provides a full-stack IoT security engine that can be embedded into endpoints, gateways and cloud servers all the way up to authentication of users. The integrated solution composes of software modules and advanced services.

It secures the industrial control systems through several services including pre-boot verification of firmware; strong cryptography to validate the BIOS, certificates based signing and verification, and platform attestation.

Mocana also launched an IoT Trust Engine which is “an abstraction layer that leverages new security features from chip makers like ARM Ltd. and Intel Corp., coupled with automatic certificate management”, wrote Duncan Riley of Silicon Angle. From system boot up to control system operations, the platform is a unified cyber security solution for IIoT.

The company’s Total Equity Funding $ 76.26M in 8 Rounds from 10 Investors. It also includes a debt-financed round of $ 4.47M in Jan 2015. Postscapes IoT Security Guide can be used to find and compare companies offering IoT security services. Other companies that recently launched their IoT security solutions include Cloudflare Orbit, an IoT security alliance, and Homeland Security Department investing in five IoT security startups.


Postscapes: Tracking the Internet of Things