Abstract
This paper proposes a hardware accelerator design, called object recognition and classification hardware accelerator on resistive devices, which processes object recognition tasks inside emerging nonvolatile memory. The in-memory processing dramatically lowers the overhead of data movement, improving overall system efficiency. The proposed design accelerates key subtasks of image recognition, including text, face, pedestrian, and vehicle recognition. The evaluation shows significant improvements on performance and energy efficiency as compared to state-of-the-art processors and accelerators.
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