For the past few years, Google has been building its own TPU (Tensor Processing Units) to handle various processing tasks related to artificial intelligence and machine learning. Google first announced the existence of TPUs in 2016, but said it had been using them internally for more than a year. The company’s second-generation TPUs have made the news recently for significantly improved performance, and the third-generation hardware will apparently continue that trend.
According to Google CEO Sundar Pichai, the new TPU 3.0 pods are 8x more powerful than the Google TPU 2.0 pods that we’ve previously covered, and Pichai declared that they’re power-hungry enough to need water cooling — something previous TPUs simply haven’t required. TechCrunch’s image shows a copper-plated cooler system, with water from the same pipe run through all four cooling plates.
So what do we know about TPU 3.0? Not much — but we can make a few educated guesses. According to Google’s own documentation, TPU 1.0 was built on a 28nm process node at TSMC, clocked at 700MHz, and consumed 40W of power. Each TPU PCB connected via PCIe 3.0 x16.
TPU 2.0 made some significant changes. Unlike TPU v1, which could only handle 8-bit integer operations, Google added support for single-precision floats in TPU v2 and added 8GB of HBM memory to each TPU to improve performance. A TPU cluster consists of 180 TFLOPS of total computational power, 64GB of HBM memory, and 2,400GB/s of memory bandwidth in total (the last thrown in purely of the purposes of making PC enthusiasts moan with envy).
Unlike TPU v1, which used 3.5-inch drive bays as its form factor, TPU v2 was welded together in groups of four ASIC chips. Google currently deploys TPUs in clusters of up to 64 boards, at 11.5 PFLOPS per cluster and 4TB of total HBM storage. Power consumption requirements were already estimated to be pretty high with last year’s model, and these solutions consume even more power, so the switch to water cooling makes sense — it’s probably the only way to deal with the heat output, especially if Google is stuffing 64 TPUs into a single cluster.
No word yet on other advanced capabilities of the processors, and they are supposedly still for Google’s own use, rather than wider adoption. Pichai claims TPU v3 can handle 100 PFLOPS, but that has to be the clustered variant, unless Google is also rolling out a new tentative project we’ll call “Google Stellar-Equivalent Thermal Density.” We would’ve expected to hear about it, if that was the case. As more companies flock to the AI / ML banner, expect to see more firms throwing their hats into this proverbial ring.
How to Install Windows 10 in a Virtual Machine
If you have to deal with files you can't trust, need to test multiple OS installations on the same system, or otherwise need access to the OS without wanting to use it as a daily driver, here's how to install Windows 10 in a virtual machine.
Windows 11 Will Launch on Oct. 5, but Only for Select Machines
Microsoft says Windows 11 will start hitting compatible PCs on October 5th. However, you might have to wait much, much longer to install it on your machine.
Google’s AutoML Creates Machine Learning Models Without Programming Experience
The gist of Cloud AutoML is that almost anyone can bring a catalog of images, import tags for the images, and create a functional machine learning model based on that.