Following Google's release of a paper detailing how its tensor processing units (TPUs) beat 2015 CPUs and GPUs [soylentnews.org] at machine learning inference tasks, Nvidia has countered with results [tomshardware.com] from its Tesla P40 [anandtech.com]:
Google's TPU went online in 2015, which is why the company compared its performance against other chips that it was using at that time in its data centers, such as the Nvidia Tesla K80 GPU and the Intel Haswell CPU.
Google is only now releasing the results, possibly because it doesn't want other machine learning competitors (think Microsoft, rather than Nvidia or Intel) to learn about the secrets that make its AI so advanced, at least until it's too late to matter. Releasing the TPU results now could very well mean Google is already testing or even deploying its next-generation TPU.
Nevertheless, Nvidia took the opportunity to show that its latest inference GPUs, such as the Tesla P40, have evolved significantly since then, too. Some of the increase in inference performance seen by Nvidia GPUs is due to the company jumping from the previous 28nm process node to the 16nm FinFET node. This jump offered its chips about twice as much performance per Watt.
Nvidia also further improved its GPU architecture for deep learning in Maxwell, and then again in Pascal. Yet another reason for why the new GPU is so much faster for inferencing is that Nvidia's deep learning and inference-optimized software [tomshardware.com] has improved significantly as well.
Finally, perhaps the main reason for why the Tesla P40 can be up to 26x faster than the old Tesla K80, according to Nvidia, is because the Tesla P40 supports INT8 computation, as opposed to the FP32-only support for the K80. Inference doesn't need too high accuracy when doing calculations and 8-bit integers seem to be enough for most types of neural networks.
Google's TPUs use less power, have an unknown cost (the P40 can cost $5,700), and may have advanced considerably since 2015.
Previously: Google Reveals Homegrown "TPU" For Machine Learning [soylentnews.org]