Quantization

Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review.

The field of Tiny Machine Learning (TinyML) has gained significant attention due to its potential to enable intelligent applications on resource-constrained devices. This review provides an in-depth analysis of the advancements in efficient neural …

TinyMLOps for real-time ultra-low power MCUs applied to frame-based event classification

TinyML applications such as speech recognition, motion detection, or anomaly detection are attracting many industries and researchers thanks to their innovative and cost-effective potential. Since tinyMLOps is at an even earlier stage than MLOps, the …

One Bit Quantization for Embedded Systems

Towards Universal 1-bit Weight Quantization of Neural Networks with end-to-end deployment on ultra-low power sensors

We focus on a promising 1–bit weight quantization approach for neural networks that optimizes the model and the weights at the same time during training. It has a low training overhead and is hassle–free, scalable, and automatable. We show that this method 2 can generalize to n–bits quantization, granted sufficient int–n support is available on the edge device. This algorithm is model–agnostic and can integrate into any training phase of the TinyMLOps workflow, for any application.

Method for Improved Keyword Spotting