Guest Editors' Introduction: Machine Intelligence at the Edge

Recent years have seen widespread application of machine learning (ML) to a diverse range of industries and problem domains. By taking advantage of the availability of massive amounts of data and scalable compute resources, ML methods are able to outperform traditional hand-tuned models on today’s wide-range of AI tasks, particularly in the settings of server computing and/or cloud computing. However, the success of ML in sensing applications such as object detection and speech recognition has also driven a demand for such technology (both training and inference) in edge settings, for applications such as autonomous vehicles, mobile devices, and embedded/Internet of Things (IoT) systems. Unfortunately, most existing ML models, hardware, and frameworks are tailored toward the server and/or cloud computing environments and are ill-equipped for edge computing. State- of-the-art ML systems in industry today already use custom frameworks, algorithms, and hardware built for a server infrastructure. Addressing the unique challenges of ML at the edge will similarly require specialization, codesign, and integration of domain knowledge for the edge across the computing stack. This timely special issue IEEE Design&Test of called for novel research on ML models, hardware architectures, programming tools, and design methodologies for ML at the edge.