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MIT aims to speed up robot movements to match robot thoughts using custom chips

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MIT researchers are looking to address the significant gap between how quickly robots can process information (relatively slowly), and how fast they can move (very quickly thanks to modern hardware advances), and they’re using something called ‘robomorphic computing’ to do it. The method, designed by MIT Computer Science and Artificial Intelligence (CSAIL) graduate Dr. Sabrina Neuman, results in custom computer chips that can offer hardware acceleration as a means to faster response times.

Custom-built chips tailored to a very specific purpose are not new – if you’re using a modern iPhone, you have one in that device right now. But they have become more popular as companies and technologists look to do more local computing on devices with more conservative power and computing constraints, rather than round-tripping data to large data centers via network connections.

In this case, the method involves creating hyper-specific chips that are designed based on a robot’s physical layout and and its intended use. By taking into account the requirements a robot has in terms of its perception of its surroundings, its mapping and understanding of its position within those surroundings, and its motion planning resulting from said mapping and its required actions, researchers can design processing chips that greatly increase the efficiency of that last stage by supplementing software algorithms with hardware acceleration.

The classic example of hardware acceleration that most people encounter on a regular basis is a graphics processing unit, or GPU. A GPU is essentially a processor designed specifically for the task of handling graphical computing operations – like display rendering and video playback. GPUs are popular because almost all modern computers run into graphics-intensive applications, but custom chips for a range of different functions have become much more popular lately thanks to the advent of more customizable and efficient small-run chip fabrication techniques.

Here’s a description of how Neuman’s system works specifically in the case of optimizing a hardware chip design for robot control, per MIT News:

The system creates a customized hardware design to best serve a particular robot’s computing needs. The user inputs the parameters of a robot, like its limb layout and how its various joints can move. Neuman’s system translates these physical properties into mathematical matrices. These matrices are “sparse,” meaning they contain many zero values that roughly correspond to movements that are impossible given a robot’s particular anatomy. (Similarly, your arm’s movements are limited because it can only bend at certain joints — it’s not an infinitely pliable spaghetti noodle.)

The system then designs a hardware architecture specialized to run calculations only on the non-zero values in the matrices. The resulting chip design is therefore tailored to maximize efficiency for the robot’s computing needs. And that customization paid off in testing.

Neuman’s team used an field-programmable gate array (FPGA), which is sort of like a midpoint between a fully custom chip and an off-the-shelf CPU, and it achieved significantly better performance than the latter. That means that were you to actually custom manufacture a chip from scratch, you could expect much more significant performance improvements.

Making robots react faster to their environments isn’t just about increase manufacturing speed and efficiency – though it will do that. It’s also about making robots even safer to work with in situations where people are working directly alongside and in collaboration with them. That remains a significant barrier to more widespread use of robotics in everyday life, meaning this research could help unlock the sci-fi future of humans and robots living in integrated harmony.

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Personal skin problems leads founder to launch skincare startup Nøie, raises $12M Series A

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Inspired by his own problems with skin ailments, tech founder Daniel Jensen decided there had to be a better way. So, using an in-house tech platform, his Copenhagen-based startup Nøie developed its own database of skin profiles, to better care for sensitive skin.

Nøie has now raised $12m in a Series A funding round led by Talis Capital, with participation from Inventure, as well as existing investors including Thomas Ryge Mikkelsen, former CMO of Pandora, and Kristian Schrøder Hart-Hansen, former CEO of LEO Pharma’s Innovation Lab.

Nøie’s customized skincare products target sensitive skin conditions including acne, psoriasis and eczema. Using its own R&D, Nøie says it screens thousands of skincare products on the market, selects what it thinks are the best, and uses an algorithm to assign customers to their ‘skin family’. Customers then get recommendations for customized products to suit their skin.

Skin+Me is probably the best-known perceived competitor, but this is a prescription provider. Noie is non-prescription.

Jensen said: “We firmly believe that the biggest competition is the broader skincare industry and the consumer behavior that comes with it. I truly believe that in 2030 we’ll be surprised that we ever went into a store and picked up a one-size-fits-all product to combat our skincare issues, based on what has the nicest packaging or the best marketing. In a sense, any new company that emerges in this space are peers to us: we’re all working together to intrinsically change how people choose skincare products. We’re all demonstrating to people that they can now receive highly-personalized products based on their own skin’s specific needs.”

Of his own problems to find the right skincare provider, he said: “It’s just extremely difficult to find something that works. When you look at technology, online, and all our apps and everything, we got so smart in so many areas, but not when it comes to consumer skin products. I believe that in five or 10 years down the line, you’ll be laughing that we really used to just go in and pick up products just off the shelf, without knowing what we’re supposed to be using. I think everything we will be using in the bathroom will be customized.”

Beatrice Aliprandi, principal at Talis Capital, said: “For too long have both the dermatology sector and the skincare industry relied on the outdated ‘one-size-fits-all’ approach to addressing chronic skin conditions. By instead taking a data-driven and community feedback approach, Nøie is building the next generation of skincare by providing complete personalization for its customers at a massive scale, pioneering the next revolution in skincare.”

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Comms expert and VC Caryn Marooney will detail how to get attention at TC Early Stage

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We’re thrilled to announce Caryn Marooney is speaking at our upcoming TechCrunch Early Stage virtual event in July. She spoke with us last year and we had to have her back.

Just look at her resume. She was the co-founder and CEO of The Outcast Agency, one of Silicon Valley’s best-regarded public relations firms. She left her company to serve as VP of Global Communication at Facebook, which she did for eight years, overseeing communication for Facebook, Instagram, WhatsApp and Oculus. In 2019 she joined Coatue Management as a general partner, where she went on to invest in Startburst, Supabase, Defined Networks and others.

Needless to say, Marooney is one of the Valley’s experts on getting people’s attention — a skill that’s critical when running a startup, nonprofit or school bake sale.

She said it best last year: “People just fundamentally aren’t walking around caring about this new startup — actually, nobody does.” So how do you get people to care? That’s the trick and why we’re having her back to speak on this evergreen topic.

Watch her presentation from 2020 here. It’s fantastic.

One of the great things about TC Early Stage is that the show is designed around breakout sessions, with each speaker leading a chat around a specific startup core competency (like fundraising, designing a brand, mastering the art of PR and more). Moreover, there is plenty of time for audience Q&A in each session.

Pick up your ticket for the event, which goes down July 8 and 9, right here. And if you do it today, you’ll save a cool $100 off of your registration.


 

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Lightmatter’s photonic AI ambitions light up an $80M B round

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AI is fundamental to many products and services today, but its hunger for data and computing cycles is bottomless. Lightmatter plans to leapfrog Moore’s law with its ultra-fast photonic chips specialized for AI work, and with a new $80M round the company is poised to take its light-powered computing to market.

We first covered Lightmatter in 2018, when the founders were fresh out of MIT and had raised $11M to prove that their idea of photonic computing was as valuable as they claimed. They spent the next three years and change building and refining the tech — and running into all the hurdles that hardware startups and technical founders tend to find.

For a full breakdown of what the company’s tech does, read that feature — the essentials haven’t changed.

In a nutshell, Lightmatter’s chips perform certain complex calculations fundamental to machine learning in a flash — literally. Instead of using charge, logic gates, and transistors to record and manipulate data, the chips use photonic circuits that perform the calculations by manipulating the path of light. It’s been possible for years, but until recently getting it to work at scale, and for a practical, indeed a highly valuable purpose has not.

Prototype to product

It wasn’t entirely clear in 2018 when Lightmatter was getting off the ground whether this tech would be something they could sell to replace more traditional compute clusters like the thousands of custom units companies like Google and Amazon use to train their AIs.

“We knew in principle the tech should be great, but there were a lot of details we needed to figure out,” CEO and co-founder Nick Harris told TechCrunch in an interview. “Lots of hard theoretical computer science and chip design challenges we needed to overcome… and COVID was a beast.”

With suppliers out of commission and many in the industry pausing partnerships, delaying projects, and other things, the pandemic put Lightmatter months behind schedule, but they came out the other side stronger. Harris said that the challenges of building a chip company from the ground up were substantial, if not unexpected.

A rack of Lightmatter servers.

Image Credits: Lightmatter

“In general what we’re doing is pretty crazy,” he admitted. “We’re building computers from nothing. We design the chip, the chip package, the card the chip package sits on, the system the cards go in, and the software that runs on it…. we’ve had to build a company that straddles all this expertise.”

That company has grown from its handful of founders to more than 70 employees in Mountain View and Boston, and the growth will continue as it brings its new product to market.

Where a few years ago Lightmatter’s product was more of a well-informed twinkle in the eye, now it has taken a more solid form in the Envise, which they call a ‘general purpose photonic AI accelerator.” It’s a server unit designed to fit into normal datacenter racks but equipped with multiple photonic computing units, which can perform neural network inference processes at mind-boggling speeds. (It’s limited to certain types of calculations, namely linear algebra for now, and not complex logic, but this type of math happens to be a major component of machine learning processes.)

Harris was reticent to provide exact numbers on performance improvements, but more because those improvements are increasing than that they’re not impressive enough. The website suggests it’s 5x faster than an NVIDIA A100 unit on a large transformer model like BERT, while using about 15 percent of the energy. That makes the platform doubly attractive to deep-pocketed AI giants like Google and Amazon, which constantly require both more computing power and who pay through the nose for the energy required to use it. Either better performance or lower energy cost would be great — both together is irresistible.

It’s Lightmatter’s initial plan to test these units with its most likely customers by the end of 2021, refining it and bringing it up to production levels so it can be sold widely. But Harris emphasized this was essentially the Model T of their new approach.

“If we’re right, we just invented the next transistor,” he said, and for the purposes of large-scale computing, the claim is not without merit. You’re not going to have a miniature photonic computer in your hand any time soon, but in datacenters, where as much as 10 percent of the world’s power is predicted to go by 2030, “they really have unlimited appetite.”

The color of math

A Lightmatter chip with its logo on the side.

Image Credits: Lightmatter

There are two main ways by which Lightmatter plans to improve the capabilities of its photonic computers. The first, and most insane sounding, is processing in different colors.

It’s not so wild when you think about how these computers actually work. Transistors, which have been at the heart of computing for decades, use electricity to perform logic operations, opening and closing gates and so on. At a macro scale you can have different frequencies of electricity that can be manipulated like waveforms, but at this smaller scale it doesn’t work like that. You just have one form of currency, electrons, and gates are either open or closed.

In Lightmatter’s devices, however, light passes through waveguides that perform the calculations as it goes, simplifying (in some ways) and speeding up the process. And light, as we all learned in science class, comes in a variety of wavelengths — all of which can be used independently and simultaneously on the same hardware.

The same optical magic that lets a signal sent from a blue laser be processed at the speed of light works for a red or a green laser with minimal modification. And if the light waves don’t interfere with one another, they can travel through the same optical components at the same time without losing any coherence.

That means that if a Lightmatter chip can do, say, a million calculations a second using a red laser source, adding another color doubles that to two million, adding another makes three — with very little in the way of modification needed. The chief obstacle is getting lasers that are up to the task, Harris said. Being able to take roughly the same hardware and near-instantly double, triple, or 20x the performance makes for a nice roadmap.

It also leads to the second challenge the company is working on clearing away, namely interconnect. Any supercomputer is composed of many small individual computers, thousands and thousands of them, working in perfect synchrony. In order for them to do so, they need to communicate constantly to make sure each core knows what other cores are doing, and otherwise coordinate the immensely complex computing problems supercomputing is designed to take on. (Intel talks about this “concurrency” problem building an exa-scale supercomputer here.)

“One of the things we’ve learned along the way is, how do you get these chips to talk to each other when they get to the point where they’re so fast that they’re just sitting there waiting most of the time?” said Harris. The Lightmatter chips are doing work so quickly that they can’t rely on traditional computing cores to coordinate between them.

A photonic problem, it seems, requires a photonic solution: a wafer-scale interconnect board that uses waveguides instead of fiber optics to transfer data between the different cores. Fiber connections aren’t exactly slow, of course, but they aren’t infinitely fast, and the fibers themselves are actually fairly bulky at the scales chips are designed, limiting the number of channels you can have between cores.

“We built the optics, the waveguides, into the chip itself; we can fit 40 waveguides into the space of a single optical fiber,” said Harris. “That means you have way more lanes operating in parallel — it gets you to absurdly high interconnect speeds.” (Chip and server fiends can find that specs here.)

The optical interconnect board is called Passage, and will be part of a future generation of its Envise products — but as with the color calculation, it’s for a future generation. 5-10x performance at a fraction of the power will have to satisfy their potential customers for the present.

Putting that $80M to work

Those customers, initially the “hyper-scale” data handlers that already own datacenters and supercomputers that they’re maxing out, will be getting the first test chips later this year. That’s where the B round is primarily going, Harris said: “We’re funding our early access program.”

That means both building hardware to ship (very expensive per unit before economies of scale kick in, not to mention the present difficulties with suppliers) and building the go-to-market team. Servicing, support, and the immense amount of software that goes along with something like this — there’s a lot of hiring going on.

The round itself was led by Viking Global Investors, with participation from HP Enterprise, Lockheed Martin, SIP Global Partners, and previous investors GV, Matrix Partners and Spark Capital. It brings their total raised to about $113 million; There was the initial $11M A round, then GV hopping on with a $22M A-1, then this $80M.

Although there are other companies pursuing photonic computing and its potential applications in neural networks especially, Harris didn’t seem to feel that they were nipping at Lightmatter’s heels. Few if any seem close to shipping a product, and at any rate this is a market that is in the middle of its hockey stick moment. He pointed to an OpenAI study indicating that the demand for AI-related computing is increasing far faster than existing technology can provide it, except with ever larger datacenters.

The next decade will bring economic and political pressure to rein in that power consumption, just as we’ve seen with the cryptocurrency world, and Lightmatter is poised and ready to provide an efficient, powerful alternative to the usual GPU-based fare.

As Harris suggested hopefully earlier, what his company has made is potentially transformative in the industry and if so there’s no hurry — if there’s a gold rush, they’ve already staked their claim.

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