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FPGA-based edge servers are used in many applications in smart cities, hospitals, retail, etc. Equipped with heterogeneous FPGA-based accelerator cards, the servers can be implemented with multiple tasks including efficient video prepossessing, machine learning algorithm acceleration, etc. These servers are required to implement inference during the daytime while re-training the model during the night to adapt to new environments, domains, or new users. During the re-training, conventionally, the incoming data are transmitted to the cloud, and then the updated machine learning models will be transferred back to the edge server. Such a process is inefficient and cannot protect users’ privacy, so it is desirable for the models to be directly trained on the edge servers. Deploying convolutional neural network (CNN) training on heterogeneous resource-constrained FPGAs is challenging since it needs to consider both the complex data dependency of the training process and the communication bottleneck among different FPGAs. Previous multi-accelerator training algorithms select optimal scheduling strategies for data parallelism, tensor parallelism, and pipeline parallelism. However, pipeline parallelism cannot deal with batch normalization (BN) which is an essential CNN operator, while purely applying data parallelism and tensor parallelism suffers from resource under-utilization and intensive communication costs. In this work, we propose MTrain, a novel multi-accelerator training scheduling strategy that transfers the training process into a multi-branch workflow, thus independent sub-operations of different branches are executed on different training accelerators in parallelism for better utilization and reduced communication overhead. Experimental results show that we can achieve efficient CNN training on heterogeneous FPGA-based edge servers with 1.07x-2.21x speedup under 15 GB/s peer-to-peer bandwidth compared to the state-of-the-art work. 

Purpose:Complex scientific problems, including those facing the discipline of communication sciences and disorders (CSD), require interdisciplinary teams of scientists who bring diverse perspectives, knowledge, and skills. According to a recent survey, team science is not yet widely practiced by CSD researchers. This viewpoint describes a current interdisciplinary team science project that addresses a challenging problem for CSD practitioners: meeting the needs of young children with speech and language disabilities for screening and intervention using artificial intelligence–augmented technologies. Method:The article draws from the research literature on the science of team science to describe common challenges faced by interdisciplinary teams and recommended practices to resolve the challenges. Throughout, we provide examples from the National Artificial Intelligence Institute for Exceptional Education to illustrate team science challenges and how they can be addressed. Conclusions:Readers are encouraged to embrace interdisciplinary teamwork to advance the science of CSD. We recommend seeking out training in team science, advocating for professional development opportunities, and institutional support for team science to maximize its benefits for the field.