Edge TPU

The Edge TPU is a small ASIC designed by Google that provides high performance ML inferencing for low-power devices. For example, it can execute state-of-the-art mobile vision models such as MobileNet V2 at almost 400 FPS, in a power efficient manner. 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. 

Cloud TPUs run in a Google data center, and therefore offer very high computational speeds—an individual v3 Cloud TPU can perform 420 trillion floating-point operations per second (420 teraflops). Cloud TPUs are ideal when you’re training large, complex ML models—for example, models that might take weeks to train on other hardware can converge in mere hours on Cloud TPUs.Whereas, the Edge TPU is designed for small, low-power devices, and is primarily intended for model inferencing (although it can accelerate some types of transfer learning). So although the computational speed of the Edge TPU is a fraction of the speed on a Cloud TPU, the Edge TPU is ideal when you want on-device ML inferencing that’s extremely fast and power-efficient. Edge TPU complements CPUs, GPUs, FPGAs, and other ASIC solutions for running AI at the edge. ShuraCore provides software development services for the Edge TPU. We make the Edge TPU happen.