The Battle of AI Processors Begins in 2018: Page 4 of 4

2018 will be the start of what could be a longstanding battle between chipmakers to determine who creates the hardware that artificial intelligence lives on.

TPUs have also proven themselves in some very high-end AI applications. TPUs are the brains behind Google's famous AlphaGo AI which defeated world champion Go players last year. Recently, AlphaGo took a massive leap forward by demonstrating that it can teach itself to a level of mastery in a comparatively short time. With only months of training, the latest version of AlphaGo, AlphaGo Zero, was able to teach itself to a level of competency that far surpassed that of human experts. It did the same for chess (a complex game, but exponentially less so than Go) in only a matter of hours.

FPGAs – The Dark Horse in the AI Race

So, that's it, then: TPUs are the future of AI, right? Not so fast. While Nvidia, Google, and, to some degree Intel, are all focused on delivering AI at the edge – having AI processing happen on-board a device as opposed to the cloud – Microsoft claims its data centers can deliver high-performance, cloud-based AI comparable to, and possibly exceeding, any edge-based AI using an unexpected source – FPGAs. Codenamed Project Brainwave, Microsoft believes a FPGA-based solution will be superior to any offered by a CPU, GPU, or TPU in terms of scalability and flexibility.

Microsoft's Project Brainwave performed at 39.5 teraflops with less than one millisecond of latency when run on Intel Stratix 10 FPGAs. (Image source: Microsoft / Intel).

Whereas processor-based solutions are, in some way, restricted to specific tasks by virtue of their design, FPGAs are proposed to offer easier upgrades and improved performance over other options because of their flexibility and re-programmability. According to Microsoft, when run on Intel Stratix 10 FPGAs Microsoft's Project Brainwave performed at 39.5 teraflops with less than one millisecond of latency.

Whether FPGAs offer the best option for AI is as debatable as any other. Microsoft cites the high production costs of creating AI-specific ASICs as being too prohibitive, while others will say FPGAs will never fully achieve the performance of chip designed specifically for AI.

In a paper presented in March at the International Symposium on Field Programmable Gate Arrays (ISFPGA) a group of researchers from Intel's Accelerator Architecture Lab evaluated two generations of Intel FPGAs (the Arria10 and Stratix 10) against the Nvidia Titan X Pascal (the predecessor to the Titan V) in handling deep neural network (DNN) algorithms. According to the Intel researchers, “Our results show that Stratix 10 FPGA is 10%, 50%, and 5.4x better in performance (TOP/sec) than Titan X Pascal GPU on [matrix multiply] (GEMM) operations for pruned, Int6, and binarized DNNs, respectively. ... On Ternary-ResNet, the Stratix 10 FPGA can deliver 60% better performance over Titan X Pascal GPU, while being 2.3x better in performance/watt. Our results indicate that FPGAs may become the platform of choice for accelerating next-generation DNNs.”

Who Wears the Crown?

At this particular point in time its hard not to argue that GPUs are king when it comes to AI chips in terms of overall performance. That doesn't mean, however, that companies like Nvidia and AMD should rest on their laurels, confident that they hold the best solution. Competitors like Microsoft have a vested interest in maintaining their own status quo (Microsoft's data centers are FPGA-based) and turning AI consumers to their point of view.

What's more the company that comes out on top may not be the one with the best hardware so much as the one whose hardware ends up inside of the best application. While autonomous cars are looking to be the killer app that breaks AI into the wider public consciousness, it's too early to be definite. It could be an advancement in robotics, manufacturing, or even entertainment that really pushes AI through. And that's not to discount emerging applications that haven't even been reported or developed yet.

When the smoke clears it may not be one company or even one processor that dominates the AI landscape. We could see a future that shifts away from a one-size-fits-all approach to AI hardware and see a more splintered market where the hardware varies by application. Time will tell, but all of our devices will be a lot smarter once we get there.  


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Chris Wiltz is a senior editor at  Design News  covering emerging technologies including VR/AR, AI, and robotics.

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