Coral summer updates: Post-training quant support, TF Lite delegate, and new models!

Coral's recent updates highlight advancements in local AI capabilities, including enhanced post-training quantization support, new APIs for transfer learning, and innovative models optimized for the Edge TPU. The compiler now supports full integer post-training quantization, allowing models to be fully quantized to 8-bit integers, which not only reduces model size but also enables acceleration via Coral's Edge TPU. The Edge TPU Python library update introduces a backpropagation API for near-real-time transfer learning, while the existing imprinting API allows for quick retraining of model classes. Additionally, a new TensorFlow Lite delegate for the Edge TPU lets users accelerate models using the TensorFlow Lite interpreter API. Notably, Coral collaborated with the Edge TPU and AutoML teams to release the EfficientNet-EdgeTPU models, which offer high classification accuracy in a compact form optimized for the Edge TPU. Arrow Electronics provides a student-teacher discount to encourage educational use of Coral boards, and Coral invites ongoing feedback to refine its platform further.