Since the last case study post, questions have been asked about what should be done in order to easier get models running on low power platforms. It’s a big topic, but the most effective steps start right from the beginning in the model design phase. You likely have heard of “quantization aware training”, then “deployment aware design” is the same concept, but applied during model design, for the purpose of smooth deployment at the end. Check compatibility When the initial de

The project: Company A developed a magical speech processing Neural Network model, model S, and would like to deploy it onto a battery powered wearable device. The tasks are split as MLSP.ai to deliver a deployment friendly implementation of model S and company A is responsible for model training and final product integration. Starting point: Model S fully developed in PyTorch, verified for streaming, in floating-point numerical format. Target wearable platform, well prove

A novel solution for Edge AI models deployment that addresses major pain points: high overhead, model IP protection and system...