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modern mobile applications such as virtual reality and extended reality applications have the common requirements of needing to run multiple deep neural networks or dnns fortunately recent mobile devices come equipped with multiple processors that are capable of running such dnns however existing mobile frameworks such as tensorflow lite and mace are geared towards optimizing the execution of a single dnn on a single processor a general-purpose framework that can utilize various processors to serve multi-dna workloads is missing we propose band a mobile inference system that neatly fills in this hole band is able to take advantage of the heterogeneous processors available on a mobile device to satisfy the multi dna inference requests of applications instead of running a dnn as a whole band partitions dna graphs into subgraphs and dynamically determines which subgraphs to use to process that dnn this allows band to devise a multi-dnn execution schedules that efficiently utilize processo