EfficieNN

CompOFA improves the speed, cost, and usability of jointly training models for many deployment targets. By highlighting insights on model design and system deployment, we try to address an important problem for real-world usability of DNNs.

It’s no surprise that modern deep-learning libraries have production-level, highly-optimized implementations of most operations. But just what is the black magic that these libraries use that we mere mortals don’t? What exactly does one do to “optimize” or accelerate neural networks operations?

Deploying efficient neural nets on mobiles is becoming increasingly important. This post explores the concept of quantized inference, and how it works in TensorFlow Lite.