APC_Australia_Issue_442_June_2017

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technotes » dawn of the accelerators Diligent Arty is a US$99 DIY development kit to create your own FPGA design. had found FPGAs could replicate original NES console hardware and the concept has since gone commercial. The retroUSB AVS is a NES-class console that runs NES cartridges better than the original NES console itself. It outputs 720p video through an HDMI output, You can make your hardware using FPGA boards from Adafruit.com Intel’s 4004 processor, the first CPU chip. (Thomas Nguyen, CC BY-SA 4.0). but there’s no actual Nintendo hardware on-board — it’s all powered by an FPGA chip. And it isn’t alone — Analogue created 2016’s well-received Analogue NT NES console, reportedly built using remaining stocks of genuine Famicom (Nintendo) hardware made over 30 This Intel Atom chip comes with a built-in FPGA programmable block. years ago. However, Analogue recently announced a new smaller and cheaper NT Mini which claims to maintain full compatibility with NES/Famicom cartridges, but no longer relies on ‘new old-stock’ hardware. Again, it uses an FPGA chip to mimic the features of the original Nintendo hardware, but goes further to support extras like 1080p output over HDMI. The NES Classic Mini might give you 30 games for $99, but the US$449 NT Mini’s compatibility reportedly ensures it can handle over 2,000 NES-compatible game cartridges. More importantly, it shows that FPGAs not only can replicate retired hardware, they’re sufficiently advanced to create custom CPUs. THE ACCELERATED BOOM However, the spotlight shining on FPGA tech over the last few years is less about retro-gaming and more about the boom in machine learning, AI and the evergrowing need for speed. FPGAs and ASICs are often called ‘accelerators’. While FPGAs can be made to emulate genuine CPUs for gaming consoles, they’re also known for their ability to work in parallel, plus their very low delay or ‘latency’ times. One application where latencies have become critical is financial markets. Share trading took a major leap into the future in 1971 when the US NASDAQ became the first computer-traded share market. These days, computers aren’t just making trades, they’re deciding what, 20 www.apcmag.com
when and how much to trade in what’s known as ‘high-frequency trading’ (HFT). A key requirement of HFT is speed — using machine learning to understand market data in real time to make high-speed buy/sell transactions. PROJECT CATAPULT That latency and parallelism capability has also seen another major market player show serious interest in FPGA. Back in 2011, Microsoft launched Project Catapult, a research project to look at post-CPU technologies for cloud computing, which included FPGAs, ASICs and graphics processing units. We use GPUs at home to play games on PCs, consoles and phones, but they’re increasingly being used in business applications for their speed and parallelism. For example, Nvidia’s brand-new GeForce GTX 1080 Ti graphics card boasts 3,584 processing cores designed to crunch data for highresolution gaming at incredible speeds. But through Nvidia’s CUDA application programming interface (API) code, software developers can tap those GPUs for everything from mining Bitcoins to calculating climate models (GPUs have also been known to crack passwords). However, Microsoft ended up settling on FPGA technology and has created what it calls an ‘AI supercomputer’, linking its worldwide data centres, with FPGA chips. That tech now powers Bing searches and Microsoft’s Azure cloud computing services. TENSOR PROCESSING UNITS Meanwhile, Google might be better known for its Android operating system, but it, too, is dabbling in accelerator chips, creating a custom and still-mostly-secret ‘tensor processing unit (TPU). A ‘tensor’ is a mathematical object that can be described by ‘arrays’ or groups of numbers. A row or column of a spreadsheet can be a onedimensional array representing a firstorder tensor, a single-page spreadsheet with rows and columns can represent a two-dimensional array or second-order tensor. Google isn’t handing out too much information about how its TPU works just yet, other than it being an ASIC rather than FPGA design. That said, it has revealed that the custom ASIC delivers roughly a ten-times or ‘order of magnitude’ improvement in machine-learning performance per watt compared with traditional options, which is a massive gain. THE CPU MARKET IS CHANGING But this growing market demand for accelerator chips is the sort of thing you’d expect to make CPU makers nervous. More accelerator chips will likely mean buying less CPUs, so if you’re a CPU maker in this situation, you start thinking about combining the two. Chip manufacturers have been dabbling in mixing CPUs with FPGAs into a single chip since at least 2010 — that year, Intel embedded an Altera FPGA core into an Atom E600C chip, while FPGA maker Xilinx put a CPU from phone chip designer ARM into its FPGA chips. But with the FPGA market heating up, Intel decided to put down a hefty US$16.7 billion at the end of 2015 and buy FPGA maker Altera. And it didn’t muck around — a few months later, it revealed a Broadwell-class Xeon server processor with Altera’s Arria 10 FPGA in the same chip package. THE FUTURE OF THE CPU As cool as FPGA-powered consoles are, you don’t spend nearly US$17 billion to make chips for retro-gaming. The real goal is machine learning and data centres. It’s a commonly held belief that we’re producing data at an exponential rate — according to IBM, 90% of the world’s data was generated in just the last two years, data that’s stored in data warehouses and increasingly needs machine learning to search and Microsoft is speeding up Bing searches using FPGA technology. FPGA-powered Analogue NT Mini delivers fullcompatibility NES gaming. manage. Google says its squadron of TPUs can find every piece of text in its global Street View database in under five days and just one TPU can process over 100 million photos each day in Google Photos. Meanwhile, Microsoft says its ability to write algorithms direct to its FPGA network can save two years waiting for hardware upgrades. Now, the hardware can be upgraded in code. The rise of the accelerators will inevitably lead to the question of whether or not the end is in sight for the CPU. As more of our data — and data processing — ends up in the cloud powered by all manner of ASICs and FPGAs, there will likely be a corresponding drop in the needs for CPUs based on current tech. With research into quantum computing engines that promise processing on a revolutionary scale, some might believe current CPU tech is riding its last great wave. But the reality is that traditional CPUs will still be around for a long time to come, at least until something else comes along that can match them on a price-performance scale. Now, though, where’s that NES console gone...? www.apcmag.com 21
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Page 4 and 5: Contents Issue 442 June 2017 42 TRA
Page 6 and 7: exclusivesoftware » HOW TO DOWNLOA
Page 8 and 9: technotes » NEED TO KNOW Windows 7
Page 10 and 11: technotes » HARRY DOMANSKI LOOKS A
Page 12 and 13: technotes » GEAR WE WANT OAXIS INK
Page 14 and 15: technotes » HOW IT’S DONE About
Page 16 and 17: technotes » EPINIONS “The NBN as
Page 18 and 19: technotes » dawn of the accelerato
Page 22 and 23: thelab » LATEST REVIEWS LABS BENCH
Page 24 and 25: THE MOST FUN YOU CAN HAVE WITH YOUR
Page 26 and 27: thelab » latest reviews LABS BENCH
Page 28 and 29: The home of technology techradar.co
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Page 36 and 37: software » APPS FOR ALL THE PLATFO
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