Edge TPU
The Edge TPU is a small application-specific integrated circuit (ASIC) designed by Google to run machine learning inference on low-power devices.
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Showing 1-22 of 22 articles
Frozen v2 (Google AI chip)
Frozen v2 is the informal internal codename for a specialized artificial-intelligence inference chip that Google is reportedly developing to run its Gemini models more cheaply and with far less energy.
Google Axion
Google Axion is Google's first custom Arm-based central processing unit designed for the data center.
Google TPU 8i
Google TPU 8i is an eighth-generation Tensor Processing Unit from Google, built specifically for AI inference rather than model training.
Google TPU 8t
Google TPU 8t is the training-focused member of Google's eighth-generation Tensor Processing Unit family, previewed at Google Cloud Next on April 22, 2026.
Project Suncatcher
Project Suncatcher is a long-term research "moonshot" at Google that studies whether machine learning compute could one day be hosted in space, on constellations of solar-powered satellites carrying Google…
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 Ironwood
TPU Ironwood (listed as TPU7x in Google Cloud's documentation and also called TPU v7) is Google's seventh-generation Tensor Processing Unit
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.
TPU v4
TPU v4 is Google's fourth-generation Tensor Processing Unit, a custom application-specific integrated circuit (ASIC) that accelerates machine learning workloads in Google's data centers and on Google Cloud.
TPU v5e
Cloud TPU v5e (sometimes rendered TPU v5e or, internally at Google, TPUv5e) is the fifth-generation, efficiency-optimized member of Google's Tensor Processing Unit (TPU) family.
TPU v5p
TPU v5p (Cloud TPU v5p) is Google's fifth-generation, performance-tier Tensor Processing Unit, a custom AI accelerator that delivers 459 bfloat16 TFLOPS per chip and scales to pods of 8,960 chips (about 4.45…
Tensor Processing Unit (TPU)
A Tensor Processing Unit (TPU) is a family of custom application-specific integrated circuits developed by Google to accelerate machine-learning computation.
TensorFlow Lite (LiteRT)
TensorFlow Lite, rebranded as LiteRT (short for Lite Runtime) on September 4, 2024, is Google's open-source, on-device inference runtime that runs trained machine-learning models locally on mobile, embedded…
Trillium (TPU v6e)
Trillium, also designated TPU v6e, is the sixth-generation Tensor Processing Unit developed by Google for machine learning training and inference workloads on Cloud TPU infrastructure.