SUPERCOMPUTERS usually fill entire rooms. But the one on the fifth floor of an office building in the centre of Bristol fits in an average-sized drawer. Its 16 processors punch more than 1,600 teraflops, a measure of computer performance. This puts the machine among the world’s 100 fastest, at least when solving certain artificial-intelligence (AI) applications, such as recognising speech and images.
The computer’s processors, developed by Graphcore, a startup, are tangible proof that AI has made chipmaking exciting again. After decades of big firms such as America’s Intel and Britain’s ARM ruling the semiconductor industry, the insatiable demand for computing generated by AI has created an opening for newcomers. And it may even be big enough to allow some startups to establish themselves as big, independent firms.
New Street, a research firm, estimates that the market for AI chips could reach $30bn by 2022. That would exceed the $22bn of revenue that Intel is expected to earn this year from selling processors for server computers. It could swell further, argue the authors of a recent report by UBS, an investment bank. AI processors, they believe, will create their own demand; they allow firms to develop cleverer services and devices, which will collect even more data, generating a need for even brainier chips.
To understand what is going on it helps to make a short detour into zoology. Broadly speaking, the world of processors is populated with two kinds of animal, explains Andrew Feldman, chief executive of Cerebras, an American competitor to Graphcore. One sort of chip resembles hyenas: they are generalists designed to tackle all kinds of computing problems, much as the hyenas eat all kinds of prey. The other type is like cheetahs: they are specialists which do one thing very well, such as hunting a certain kind of gazelle.
For much of computing history, hyenas named “central processing units” (CPUs) have dominated the chip savannah. Becoming ever more powerful according to Moore’s law, the rule that the performance of processors doubles every 18 months, they were able to gobble up computing tasks, or “workloads”, in the jargon. This is largely why Intel, for instance, in the early 1990s became the world’s biggest chipmaker and stayed that way for decades.
But in recent years the world of number-crunching has changed radically. Moore’s law has started to peter out because making ever-denser chips has hit physical limits. More importantly, cloud computing has made it extremely cheap to amass huge amounts of data. Now more and more firms want to turn this asset into money with the help of AI, meaning distilling data to create offerings such as recognising faces, translating speech or predicting when machinery will break down.
Such trends have altered the chip-design habitat. First to benefit were “graphics processing units” (GPUs), a kind of hyena which are mainly made by Nvidia. Originally developed to speed up the graphics in video games, they are also good at digesting reams of data, which is a similar computational problem. But because they are insufficiently specialised, GPUs have been hitting the buffers, too. The demand for “compute”, as geeks call processing power, for the largest AI projects has been doubling every 3.5 months since 2012, according to OpenAI, a non-profit research organisation (see chart). “Hardware has become the bottleneck,” says Nigel Toon, the chief executive of Graphcore.
The response from various firms has been to design processors from the ground up with AI in mind. The result of Graphcore’s efforts is called an intelligent processing unit (IPU). This name is not just marketing: on GPUs, memory (the staging area for data) and brain (where they are processed) are kept separate—meaning that data constantly have to be ferried back and forth between the two areas, creating a bottleneck with data-heavy AI applications. To do away with it, Graphcore’s chips do not just have hundreds of mini-brains, but the memory is placed right next to it, minimising data traffic.
Graphcore’s chip can also hold entire neural networks, computational models inspired by structures in biological brains, which are used in many AI applications. Having such models, which can be immensely complex with billions of parameters, sit in the chip allows them to be “trained” more quickly—the act of feeding them with lots of data (pictures of cats, say), so they learn to recognise them. The set-up also simplifies what is known as “inference”, when the model applies what it has learned (spotting cats, for instance).
Cerebras is going further still. It is not only designing a new processor, which is similar to Graphcore’s, but a specialised AI computer as well. Putting a new chip on a circuit board, as Graphcore does, that is added into an existing system limits specialisation and optimisation because of constraints in power, cooling and communication, says Mr Feldman. But this means that he has a steeper hill to climb: while Graphcore has already delivered a first batch to customers, Cerebras has yet to announce when its product will be available.
Although Graphcore and Cerebras were early to see the need for specialised AI chips, they are by no means alone. Dozens of startups are creating what are known as “application-specific integrated circuits” (ASICs). These are meant to do inference in all kinds of connected devices, from smartphones to sensors, known as the “edge”. The processors come with trained AI models baked in, for instance to let a video camera recognise faces without having to upload the entire footage.
Big cloud-computing providers have also joined the fray, deeming AI chips important enough to develop their own. In May Google launched the third generation of its Tensor Processing Units (TPUs), the previous versions of which already power many of its services, including search and Street View. Amazon, Facebook and Microsoft, too, are developing processors. Apple, for its part, ships its iPhone X with an AI chip that helps the device recognise the owner and read his facial expressions.
Firms that ruled the world of hyenas, notably Intel, are now acquiring designers of cheetahs. It has spent billions in recent years buying AI-related startups, including Nervana Systems and Mobileye. The idea, says Gadi Singer, in charge of the firm’s AI products, is to have an entire portfolio of processors, each with its own specialisation—for neural networks, for self-driving cars and for inference at the edge.
If the history of other semiconductor markets, such as networking processors, is any guide, the new field of AI chips could consolidate before too long, perhaps with one or two processor architectures winning the day. There is already talk that big cloud-computing firms, such as Amazon, are interested in buying startups, including Cerebras and Graphcore. And incumbents are trying to catch up. Intel has developed a program that ties together all its AI chips; Nvidia has tweaked the architecture of its processors, which is said to now match the performance of Google’s TPUs.
But there are forces that push toward fragmentation. Specialisation in AI chips can go very far, just as with animals (cheetahs are the only large cats whose claws do not retract, so they are ready to accelerate and catch a gazelle at all times). Pierre Ferragu of New Street says that ever more demanding AI workloads needing special treatment, fast-evolving algorithms, and tech giants designing their custom chips all may lead to a world in which lots of processor architectures thrive.
China, too, is likely to inject more diversity. The government has plans to spend tens of billions to create a national semiconductor industry in an effort to be less dependent on Western imports. According to some estimates, hundreds of firms are developing ASICs. Alibaba has announced that it is working on its own AI chip, called Ali-NPU (which stands for neural processing unit). Cambricon, a startup based in Shanghai, recently unveiled a chip that is similar to Graphcore’s and Cerebras’s. The chip kingdom is unlikely to become a dull monoculture again anytime soon.
Japan still has great influence on global financial markets
IT IS the summer of 1979 and Harry “Rabbit” Angstrom, the everyman-hero of John Updike’s series of novels, is running a car showroom in Brewer, Pennsylvania. There is a pervasive mood of decline. Local textile mills have closed. Gas prices are soaring. No one wants the traded-in, Detroit-made cars clogging the lot. Yet Rabbit is serene. His is a Toyota franchise. So his cars have the best mileage and lowest servicing costs. When you buy one, he tells his customers, you are turning your dollars into yen.
“Rabbit is Rich” evokes the time when America was first unnerved by the rise of a rival economic power. Japan had taken leadership from America in a succession of industries, including textiles, consumer electronics and steel. It was threatening to topple the car industry, too. Today Japan’s economic position is much reduced. It has lost its place as the world’s second-largest economy (and primary target of American trade hawks) to China. Yet in one regard, its sway still holds.
This week the board of the Bank of Japan (BoJ) voted to leave its monetary policy broadly unchanged. But leading up to its policy meeting, rumours that it might make a substantial change caused a few jitters in global bond markets. The anxiety was justified. A sudden change of tack by the BoJ would be felt far beyond Japan’s shores.
One reason is that Japan’s influence on global asset markets has kept growing as decades of the country’s surplus savings have piled up. Japan’s net foreign assets—what its residents own abroad minus what they owe to foreigners—have risen to around $3trn, or 60% of the country’s annual GDP (see top chart).
But it is also a consequence of very loose monetary policy. The BoJ has deployed an arsenal of special measures to battle Japan’s persistently low inflation. Its benchmark interest rate is negative (-0.1%). It is committed to purchasing ¥80trn ($715bn) of government bonds each year with the aim of keeping Japan’s ten-year bond yield around zero. And it is buying baskets of Japan’s leading stocks to the tune of ¥6trn a year.
Tokyo storm warning
These measures, once unorthodox but now familiar, have pushed Japan’s banks, insurance firms and ordinary savers into buying foreign stocks and bonds that offer better returns than they can get at home. Indeed, Japanese investors have loaded up on short-term foreign debt to enable them to buy even more. Holdings of foreign assets in Japan rose from 111% of GDP in 2010 to 185% in 2017 (see bottom chart). The impact of capital outflows is evident in currency markets. The yen is cheap. On The Economist’s Big Mac index, a gauge based on burger prices, it is the most undervalued of any major currency.
Investors from Japan have also kept a lid on bond yields in the rich world. They own almost a tenth of the sovereign bonds issued by France, for instance, and more than 15% of those issued by Australia and Sweden, according to analysts at J.P. Morgan. Japanese insurance companies own lots of corporate bonds in America, although this year the rising cost of hedging dollars has caused a switch into European corporate bonds. The value of Japan’s holdings of foreign equities has tripled since 2012. They now make up almost a fifth of its overseas assets.
What happens in Japan thus matters a great deal to an array of global asset prices. A meaningful shift in monetary policy would probably have a dramatic effect. It is not natural for Japan to be the cheapest place to buy a Big Mac, a latté or an iPad, says Kit Juckes of Société Générale. The yen would surge. A retreat from special measures by the BoJ would be a signal that the era of quantitative easing was truly ending. Broader market turbulence would be likely. Yet a corollary is that as long as the BoJ maintains its current policies—and it seems minded to do so for a while—it will continue to be a prop to global asset prices.
Rabbit’s sales patter seemed to have a similar foundation. Anyone sceptical of his mileage figures would be referred to the April issue of Consumer Reports. Yet one part of his spiel proved suspect. The dollar, which he thought was decaying in 1979, was actually about to revive. This recovery owed a lot to a big increase in interest rates by the Federal Reserve. It was also, in part, made in Japan. In 1980 Japan liberalised its capital account. Its investors began selling yen to buy dollars. The shopping spree for foreign assets that started then has yet to cease.
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