Any-Precision Deep Neural Networks

We present any-precision deep neural networks (DNNs), which are trained with a new method that allows the learned DNNs to be flexible in numerical precision during inference. The same model in runtime can be flexibly and directly set to different bit-widths, by truncating the least significant bits, to support dynamic speed and accuracy trade-off. When all layers are set to low-bits, we show that the model achieved accuracy comparable to dedicated models trained at the same precision. This nice property facilitates flexible deployment of deep learning models in real-world applications, where in practice trade-offs between model accuracy and runtime efficiency are often sought. Previous literature presents solutions to train models at each individual fixed efficiency/accuracy trade-off point. But how to produce a model flexible in runtime precision is largely unexplored. When the demand of efficiency/accuracy trade-off varies from time to time or even dynamically changes in runtime, it is infeasible to re-train models accordingly, and the storage budget may forbid keeping multiple models. Our proposed framework achieves this flexibility without performance degradation. More importantly, we demonstrate that this achievement is agnostic to model architectures and applicable to multiple vision tasks. Our code is released at https://github.com/SHI-Labs/Any-Precision-DNNs.Comment: AAAI 202

Neural Networks, Precision, and Accuracy

With the rise of artificial intelligence and its integration with the healthcare sector, we see innovations such as a deep neural network that predicts optical segmentation that needs to be tracked for surgery. Machine learning, neural networks, in particular, have revolutionized the field of predictive modeling. These algorithms help us see and observe events that we have no way of seeing ourselves. Therefore, as shown by this recent breakthrough with optical surgery, neural networks can be used to predict events that happen or will happen during surgery. With this knowledge, surgeons might know where exactly to make an incision or which areas are hypersensitive to surgical tools. With that being said, are there any other avenues of surgery that can be revolutionized through neural networks. In general, how have neural networks or any other form of machine learning been deployed to maximize the precision and accuracy of surgery