AIMET is an open-source library for optimizing trained neural network models. It does this by providing advanced model compression and quantization techniques to shrink models while maintaining task accuracy. Smaller models translate into improved run-time performance and lower latency as well lower compute, memory, and power consumption.

Developers can incorporate AIMET’s advanced model compression and quantization algorithms into their PyTorch and TensorFlow model-building pipelines for automated post-training optimization, as well as for model fine-tuning. Automating these algorithms helps eliminate the need for hand-optimizing neural networks that can be time-consuming, error-prone, and difficult to repeat. And as part of our Qualcomm AI Stack, you can combine multiple software capabilities in your development.

Qualcomm Innovation Center (QuIC) open sourced AIMET on GitHub to collaborate with other leading AI researchers and to provide a simple library plugin for AI developers to utilize for state-of-the-art model efficiency performance. The cutting-edge compression and quantization techniques are based on innovative research from Qualcomm AI Research.