Cloud TPU
Cloud TPU is Google Cloud's offering of Tensor Processing Units (TPUs), the family of custom application-specific integrated circuits (ASICs) that Google builds to accelerate machine learning training and…
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Showing 1-19 of 19 articles
Edge AI
Edge AI is the practice of running artificial intelligence models directly on the device that generates the data, such as a smartphone, laptop, camera, vehicle, or IoT sensor
FLOPS
FLOPS (floating-point operations per second, also written FLOP/s) is a measure of computing speed: the number of arithmetic operations on floating-point numbers that a processor, accelerator or supercomputer…
GPU computing
GPU computing is the use of a graphics processing unit (GPU) to perform general-purpose computation that was traditionally handled by the central processing unit (CPU).
MLPerf
MLPerf is the AI industry's standard suite of benchmarks for measuring how fast machine learning systems train and run models, developed and maintained by MLCommons
MXFP4
MXFP4 (Microscaling FP4) is a 4-bit block-scaled number format for quantization in machine learning, defined in the Open Compute Project (OCP) Microscaling Formats (MX) Specification v1.0 of September 2023.
Microscaling formats
Microscaling (MX) formats are a family of low-precision number formats for machine learning in which a small block of values, normally 32 of them, shares one common scale factor while each value is stored in a…
NVFP4
NVFP4 (NVIDIA FP4) is a 4-bit floating-point number format introduced by Nvidia with the Blackwell GPU architecture.
On-device AI
On-device AI is the practice of running machine learning models on the phone, laptop, watch, or embedded board a person is actually using, instead of sending the input to a remote data center.
TPU Board
A TPU board (Tensor Processing Unit board) is a printed circuit board (PCB) that houses one or more Tensor Processing Unit chips along with associated memory, power delivery, and interconnect components.
TPU Chip
This article covers the TPU chip and its hardware. For the broader topic, see Tensor Processing Unit (TPU).
TPU Device
A TPU device is a Google-designed application-specific integrated circuit (ASIC), the physical Tensor Processing Unit chip and its host hardware
TPU Master
The TPU master is the control and coordination role in Google's Tensor Processing Unit (TPU) system that drives the actual TPU computation.
TPU Node
A TPU node is the legacy Google Cloud architecture for accessing Tensor Processing Unit (TPU) hardware, in which a user's virtual machine (VM) runs application code and communicates with a separate
TPU Pod
A TPU Pod is a single Google supercomputer built from many Tensor Processing Unit (TPU) chips wired directly to each other by a high-speed Inter-Chip Interconnect (ICI) fabric arranged as a 2D or 3D torus, so…
TPU Slice
A TPU slice is a collection of Tensor Processing Unit (TPU) chips that all sit inside the same Google Cloud TPU Pod and are connected to one another by a high-speed inter-chip interconnect (ICI).
TPU Type
The types of TPU (Tensor Processing Unit) are the successive generations of custom machine-learning accelerator that Google has built since 2015: v1, v2, v3, v4, v5e, v5p, v6e (Trillium), and v7 (Ironwood).
TPU Worker
A TPU worker is a virtual machine (VM) running Linux that has direct access to one or more Tensor Processing Unit (TPU) chips and executes the actual TPU computation on that attached hardware.
TensorRT
TensorRT is NVIDIA's software development kit (SDK) for high-performance deep learning inference on NVIDIA GPUs.