■ Abstract : The deep learning has become an important research problem in both software and hardware architecture. As the performance of deep learning models in various real-world problems far exceeds the other conventional techniques, a number of deep learning models are proposed and widely used. However, the size of deep learning models and/or the training dataset are increasing to achieve state-of-the-art performance. This puts many design challenges for hardware engineers to design energy efficient DNN accelerators either for inference or training. In the first part of this talk, I will present two research works on designing an energy-efficient inference engine: i) a pruning technique called relaxed pruning and its hardware accelerator and ii) evolutionary algorithm to search for an optimal dataflow for the inference engine. For the second part of my talk, I will present a system architecture, called deep partitioned training, that utilizes both near-storage computing and a DNN accelerator for the high-performance training of DNN models.