The Machine Learning Group at Arm

An Interview with Jem Davies, Arm Fellow and Arm’s new VP of Machine Learning.

Arm has established a new Machine Learning (ML) group. Putting this within context, machine learning is a subset of AI, and deep learning is a subset of ML. Neural networks are a way of organizing computational capabilities that are particularly effective for delivering the results that we see with machine learning. With machine learning, computers “learn” rather than get programmed. Machine learning is accomplished by feeding an extensive data set of known-good examples of what the computer scientist wants to see from the machine.


Figure 1: Deep learning, using Neural Networks (NN), attempts to model real life with data using multiple processing layers that build on each other. Examples of algorithms integral to ML include Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and others. (Credit: Arm, Jem Davies)

Arm has published some of its viewpoints about Artificial Intelligence (AI) online.

According to Jem Davies, Arm Fellow and Arm’s new VP of Machine Learning, Client Line of Business, machine learning is already a large part of video surveillance in the prevention of crime. Davies’ prior role as general manager and Fellow, Media Processing Group at Arm, is an excellent segue into ML, as Graphic Processing Units (GPUs) hold a primary role in accelerating the computational algorithms needed for ML. ML requires large amounts of good data and computational power that is fast at processing repetitive algorithms. Accelerators like GPUs and now FPGAs are used to off-load CPUs so the entire ML process is accelerated.

Davies is the kind of good-humored, experienced engineer whom everyone wants to work with; his sense of humor is just one tool in his arsenal for encouraging others with an upbeat attitude. I had an opportunity to meet with Davies at Arm TechCon. Edited excerpts of our interview follow.

Lynnette Reese (LR): Artificial Intelligence (AI) as a science has been around for a very long time.  What attributes in improved technology do you think contributed the most to the recent maturing in AI? Is it attributed to the low cost of compute power?

Jem Davies, Arm

 Jem Davies: Really, it’s the compute power that’s available at the edge. In the server, there’s no real change, but the compute power available at the edge has transformed the last five years, so that’s made a huge difference. What’s fired up interest in the neural network world is the availability of good quality data. Neural networks provide a technique that’s more than 50 years old, what we’ve got now is the training data, good, quality, correlated data. So for example, the one that sort of drove it initially was image recognition. In order to train a neural network to do image recognition, you have to have vast quantities of images that are labeled. Where are you going to find one of those? As it turns out, Google and Facebook have all of your pictures. You’ve tagged all of those pictures, and you clicked on the conditions that said they could do what they wanted with them. The increasing capability of computing, particularly in devices, has led to the explosion in data.

LR: You said that the explosion of data is the impetus for machine learning, and this is clear with image recognition, perhaps, but where else do we see this?

Davies: The computational linguists are having a field day. Nowadays we have access to all sorts of conversations that take place on the internet. You have free, easy access to this data. You want to work out how people talk to each other, look on the internet. If you are trying to work out how people talk to each other, look on the web. And it turns out that they do it in all sorts of different languages, and it’s free to take. So, the data is there.

LR: So, applying successful ML to any problem first requires good data?

Davies: If you haven’t got the data, it’s difficult to get a neural network algorithm. They are working on that; there is research being done to work using much smaller amounts of training data, which is interesting because it opens up training at the device level. We are doing training on-device now but in a relatively limited way; but you don’t need six trillion pictures of cats to accomplish cat identification.

LR: In your Arm talk about Computer Vision last year you said there were 6 million CCTVs in the U.K. What do you imagine AI will be doing with images CCTV 20 years from now? For instance, do you perceive that we can combat terrorism much more efficiently?

Davies: It is being done today. We are analyzing gait, suspicious behavior; there are patterns people have that give themselves away. This is something an observational psychologist already knows. People give themselves away by the way they stand; the way they hold themselves.

LR: What about sensing beyond visual recognition? For example, can you use an IR sensor to determine whether a facial prosthesis is in use, for example?

Davies: When engineering moves beyond the limited senses that humans possess, you can throw more information at the problem. Many activities work much better using IR than in the visible spectrum. IR poses fewer issues with shadows, for instance. One example of challenges we face with a security camera is that the camera might have to cover an area where the sun is streaming down, and there’s a shadow at the other end of the frame. If you are tracking someone from one side to the other of the frame, shadows can interfere with obtaining consistent detail in such situations. Move that to the IR domain, and it gets a whole lot easier. But why stop there? You can add all sorts of other things to it as well. Why not add radar, microwaves? You can do complete contour mapping.

LR: So, you could get very detailed with this? Adding additional sensors can give more data.

Davies: Yes, sensor fusion is the way forward. We [humans] are adding together the input from all our senses all the time. And our brains sometimes tell us, “That input doesn’t fit, just ignore it.” I can turn my head one way and think I can still see someone in my peripheral vision. But actually, you can’t. The spot right in front of you is the area that you can see in any detail. The rest is just your brain filling things in for you.

LR:  What’s Arm doing to innovate for AI?

Davies: We are doing everything; hardware, software, and working with the ecosystem. If you look at hardware, we are making our existing IP, our current processors better at executing machine learning workloads. We are doing that for our CPUs and our GPUs. On the interconnect side, things like DynamIQ [technology], enable our partners to connect other devices into SoCs containing our IP. This is a considerable amount of software because people do not want to get deep into the details.

If you look at the Caltrain example, where an image recognition model for trains was developed with data from Google images and used on a 32-bit Arm-based Raspberry Pi, it’s becoming quite easy to apply ML techniques. He just downloaded stuff off the web; he didn’t know anything about it. He’s not an image recognition specialist, he doesn’t know anything about neural networks, and, why should he?  If we [Arm] do our jobs properly, if we provide the software to people, it just works. It turns out there’s a lot of software involved; probably half my engineers are software engineers. The Arm compute library, is given away [as] open source; it has optimized routines to do all the things that go into machine learning. That is what powers the implementations on our devices. Google’s Tensorflow, Facebook’s Caffe, and others plug into that, and so you end up with force multiplier effect. We do the work, give it to the ecosystem, and Facebook has now got millions of devices that are optimized to run on Arm CPUs and Arm Mali GPUs. As you can see, there’s a lot of hardware development, software development, and a significant amount of working with the ecosystem. Everybody’s getting involved.

LR: What can you tell me about Arm’s new Machine Learning business? Do you have any industry numbers?

Davies: Industry numbers are hard to get. What I will say is that it’s going to be huge. It’s going to affect everything we do. One of the reasons why we formed the machine learning business as it is, is that it cuts across all new lines of business.

LR: Not that you should take sides, but what would you say about using FPGAs vs. GPUs in AI?

Davies: Arm doesn’t take sides. Arm always plays both sides. FPGAs are flexible; you can reconfigure the hardware to great benefit. But that comes at the cost of much less density and much more power. People [physically] get burnt touching an FPGA. For us, it’s a trade-off. If you can implement something in an FPGA that’s absolutely, specifically tailored to that problem. Presumably, it will be more efficient. But executing on an FPGA…an FPGA is bigger, much more expensive, and uses more power. Which way does that balance come down? It’s a different problem, as it comes down to it. Pretty much for anything battery powered, the answer is that FPGAs are a bust in that space. FPGAs don’t fit there; not in small, portable electronic devices. For environments that are much bigger, less power constrained, maybe there’s a place for it. However, note that both Altera and Xilinx have products with Arm cores now.

LR: What would you say to young engineers starting out today that want to go into Machine Learning?

Davies: “Come with us, we are going to change the world,” which is precisely what I said in an all-hands meeting with my group just last week. And I don’t think that’s too grand. Look at what Arm did with graphics. We started a graphics business in 2006; we had zero market share. Yet our partners shipped over a billion chips last year containing Mali Arm GPUs.

LR: Billions of people are tapping on their devices using Arm’s technology.

Davies: Yes. If I look back on what we have achieved at Arm, the many hundreds of people doing this, you can easily say that Arm has changed the world.

LR: So, Arm is not a stodgy British company? Everyone needs good talent, and Arm is changing the way we live?

Davies: Absolutely, we are a talent business. Don’t tell the accountants, but the assets of the company walk out the door every night. If you treat people well, they come back in the next morning.

LR: It sounds like Arm values employees very much.

Davies: Well, we definitely try to. Clearly, as any company, we occasionally get things wrong, but we put a lot of effort into looking after people because together we make the company. As with any company, our ambition is limited by the number of people we have. Effectively, we are no longer limited by money [due to Softbank’s vision upon acquiring Arm].

LR: So now you can build cafeterias with free food and busses to carry employees around?

Davies: Right, I am still waiting for my private jet…but seriously, that’s what I was talking about, that we are changing the world. I think [new graduates] would want to be part of that.


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