“TPUs deliver an order of magnitude higher performance per watt than all commercially available GPUs and FPGA,” said Google CEO Sundar Pichai during the company’s I/O developer conference on Wednesday.
TPUs have been a closely guarded secret of Google, but Pichai said the chips powered the AlphaGo computer that beat Lee Sedol, the world champion in the incredibly complicated game called Go.
Pichai didn’t go into details of the Tensor Processing Unit but the company did disclose a little more information in a blog posted the same day as Pichai’s revelation.
“We’ve been running TPUs inside our data centers for more than a year, and have found them to deliver an order of magnitude better-optimized performance per watt for machine learning. This is roughly equivalent to fast-forwarding technology about seven years into the future (three generations of Moore’s Law),” the blog said. “TPU is tailored to machine learning applications, allowing the chip to be more tolerant of reduced computational precision, which means it requires fewer transistors per operation. Because of this, we can squeeze more operations per second into the silicon, use more sophisticated and powerful machine learning models, and apply these models more quickly, so users get more intelligent results more rapidly.”
The tiny TPU can fit into a hard drive slot within the data center rack and has already been powering RankBrain and Street View, the blog said.
What isn’t known is what exactly the TPU is. SGI had a commercial product called the Tensor Processing Unit in its workstations in the early 2000s that appears to have been a Digital Signal Processor, or DSP. A DSP is a dedicated chip that does a repetitive, simple task extremely quickly and efficiently.
Analyst Patrick Moorhead of Moore Insights & Strategy, who attended the I/O developer conference, said, from what little Google has revealed about the TPU, he doesn’t think the company is about to abandon traditional CPUs and GPUs just yet.
“It’s not doing the teaching or learning,” he said of the TPU. “It’s doing the production or playback.”
Moorhead said he believes the TPU could be a form of chip that implements the machine learning algorithms that are crafted using more power hungry GPUs and CPUs.
As to Google’s claim that the TPU’s performance is akin to accelerating Moore’s Law by seven years, he doesn’t doubt it. He sees it as similar to the relationship between a traditional ASIC and a CPU.
ASICs are hard-coded, highly optimized chips that do one thing really well. They can’t be changed like an FPGA but offer huge performance benefits. He likened the comparison to decoding an H.265 video stream with a CPU versus an ASIC for just that task. A CPU without dedicated circuits would consume far more power than the ASIC at that job.
One issue with ASICs though is the cost and their permanent nature, he said. The only way to change the algorithm is if a bug or improvement is found to make a new one. That’s why ASICs have been traditionally the domain of entities with unlimited budgets like governments.