New Electronics - Breakthrough deep learning performance on a CPU

Deci's proprietary Automated Neural Architecture Construction (AutoNAC) technology automatically generated the new image classification models that improve all published models and deliver more than 2x improvement in runtime, coupled with improved accuracy, as compared to the most powerful models publicly available such as EfficientNets, developed by Google. While GPUs have traditionally been used in convolutional neural networks (CNNs), CPUs are a much cheaper alternative. Although it is possible to run deep learning inference on CPUs, they are significantly less powerful than GPUs and, as a result, deep learning models typically perform 3-10X slower on a CPU than on a GPU. DeciNets significantly close that performance gap so that tasks, that previously could not be carried out on a CPU because they were too resource intensive, are now possible. Additionally, these tasks will see a marked performance improvement.