  DTMM is a library designed for efficient deployment and execution of machine
learning models on weak IoT devices such as microcontroller units (MCUs). The
motivation for designing DTMM comes from the emerging field of tiny machine
learning (TinyML), which explores extending the reach of machine learning to
many low-end IoT devices to achieve ubiquitous intelligence. Due to the weak
capability of embedded devices, it is necessary to compress models by pruning
enough weights before deploying. Although pruning has been studied extensively
on many computing platforms, two key issues with pruning methods are
exacerbated on MCUs: models need to be deeply compressed without significantly
compromising accuracy, and they should perform efficiently after pruning.
Current solutions only achieve one of these objectives, but not both. In this
paper, we find that pruned models have great potential for efficient deployment
and execution on MCUs. Therefore, we propose DTMM with pruning unit selection,
pre-execution pruning optimizations, runtime acceleration, and post-execution
low-cost storage to fill the gap for efficient deployment and execution of
pruned models. It can be integrated into commercial ML frameworks for practical
deployment, and a prototype system has been developed. Extensive experiments on
various models show promising gains compared to state-of-the-art methods.
