AI at the edge
AI is pervasive today, from consumer to enterprise applications. With the explosive growth of connected devices, combined with a demand for privacy/confidentiality, low latency and bandwidth constraints, AI models trained in the cloud increasingly need to be run at the edge. Edge TPU is Google’s purpose-built ASIC designed to run AI at the edge. It delivers high performance in a small physical and power footprint, enabling the deployment of high-accuracy AI at the edge.
End-to-end AI infrastructure
Edge TPU complements Cloud TPU and Google Cloud services to provide an end-to-end, cloud-to-edge, hardware + software infrastructure for facilitating the deployment of customers' AI-based solutions.
High performance in a small physical and power footprint
Thanks to its performance, small footprint, and low power, Edge TPU enables the broad deployment of high-quality AI at the edge.
Co-design of AI hardware, software and algorithms
Edge TPU isn't just a hardware solution, it combines custom hardware, open software, and state-of-the-art AI algorithms to provide high-quality, easy to deploy AI solutions for the edge.
A broad range of applications
Edge TPU can be used for a growing number of industrial use-cases such as predictive maintenance, anomaly detection, machine vision, robotics, voice recognition, and many more. It can be used in manufacturing, on-premise, healthcare, retail, smart spaces, transportation, etc.
An open, end-to-end infrastructure for deploying AI solutions
Edge TPU enables the deployment of high-quality ML inference at the edge. It augments Google’s Cloud TPU and Cloud IoT to provide an end-to-end (cloud-to-edge, hardware + software) infrastructure to facilitate the deployment of customers' AI-based solutions. In addition to its open-source TensorFlow Lite programming environment, Edge TPU will initially be deployed with several Google AI models, combining Google's expertise in both AI and hardware.
Edge TPU complements CPUs, GPUs, FPGAs, and other ASIC solutions for running AI at the edge, which will be supported by Cloud IoT Edge.
(Devices/nodes, Gateways, Servers)
|Tasks||ML inference||ML training and inference|
|Software, services||Cloud IoT Edge, Linux OS||
Cloud ML Engine, Kubernetes Engine,
Compute Engine, Cloud IoT Core
|ML frameworks||TensorFlow Lite, NN API||
|Hardware accelerators||Edge TPU, GPU, CPU||Cloud TPU, GPU, and CPU|
Edge TPU features
This ASIC is the first step in a roadmap that will leverage Google's AI expertise to follow and reflect in hardware the rapid evolution of AI.
|Performance Example||Edge TPU enables users to concurrently execute multiple state-of-the-art AI models per frame, on a high-resolution video, at 30 frames per second, in a power-efficient manner.|
|IO Interface||PCIe, USB|
Products listed on this page are in beta. For more information on our product launch stages, see here.