AI has become ubiquitous today, from personal devices to enterprise applications; you see them everywhere. The advent of IoT, clubbed with rising demand for data privacy, low power, low latency, and bandwidth constraints has increasingly pushed for AI models to be running at the edge instead of the cloud. According to Grand View Research, the global edge artificial intelligence chips market was valued at USD 1.8 billion in 2019 and is expected to grow at a CAGR of 21.3 percent from 2020 to 2027. At this onset, Google introduced Edge TPU, also known as Coral TPU, its purpose-built ASIC for running AI at the edge.
It’s designed to give excellent performance while taking up minimal space and power. When we train an AI model, we have AI models with high storage requirements and GPU processing power. We cannot execute them on devices with low memory and processing footprints. TensorFlow Lite is useful in this situation. TensorFlow Lite is an open-source deep learning framework that runs on the Edge TPU and allows for on-device inference and AI model execution. Also, note that TensorFlow Lite is only for executing inference on edge, not training a model. For training an AI model, we must use TensorFlow.