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With the growing demand for processing higher fidelity data and the use of faster computing cores in newer hardware accelerators, modern deep neural networks (DNNs) are becoming increasingly memory intensive. A disparity between underutilized computing cores and saturated memory bandwidth has been observed in various popular DNN models. This inefficiency is caused by both the conventional treatment of DNNs as compute-intensive workloads and the lack of holistic memory access optimization in DNN models.