ORCHARD: Visual object recognition accelerator based on approximate in-memory processing

In recent years, machine learning for visual object recognition has been applied to various domains, e.g., autonomous vehicle, heath diagnose, and home automation. However, the recognition procedures still consume a lot of processing energy and incur a high cost of data movement for memory accesses. In this paper, we propose a novel hardware accelerator design, called ORCHARD, which processes the object recognition tasks inside memory. The proposed design accelerates both the image feature extraction and boosting-based learning algorithm, which are key subtasks of the state-of-the-art image recognition approaches. We optimize the recognition procedures by leveraging approximate computing and emerging non-volatile memory (NVM) technology. The NVM-based in-memory processing allows the proposed design to mitigate the CMOS-based computation overhead, highly improving the system efficiency. In our evaluation conducted on circuit- and device-level simulations, we show that ORCHARD successfully performs practical image recognition tasks, including text, face, pedestrian, and vehicle recognition with 0.3% of accuracy loss made by computation approximation. In addition, our design significantly improves the performance and energy efficiency by up to 376x and 1896x, respectively, compared to the existing processor-based implementation.