Deci announced a new set of industry-leading image classification models, dubbed DeciNets, for Intel Cascade Lake CPUs. Deci’s proprietary Automated Neural Architecture Construction (AutoNAC) technology automatically generated the new image classification models that significantly 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 the hardware of choice for running convolutional neural networks (CNNs), CPUs, already more commonly utilized for various computing tasks, would serve as a much cheaper alternative. Although it is possible to run deep learning inference on CPUs, generally they are significantly less powerful than GPUs. Consequently, deep learning models typically perform 3-10X slower on a CPU than on a GPU.
DeciNets closes the gap significantly between GPU and CPU performance for CNNs. With DeciNets, 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: by leveraging DeciNets, the gap between a model’s inference performance on a GPU versus a CPU is cut in half, without sacrificing the model’s accuracy.