Qian Lou
ABSTRACT
It is extremely challenging to deploy computing-intensive convolutional neural networks (CNNs) with rich parameters in mobile devices because of their limited computing resources and low power budgets. Although prior works build fast and energy-efficient CNN accelerators by greatly sacrificing test accuracy, mobile devices have to guarantee high CNN test accuracy for critical applications, e.g., unlocking phones by face recognitions. In this paper, we propose a 3D XPoint ReRAM-based process-in-memory architecture, 3DICT, to provide various test accuracies to applications with different priorities by lookup-based CNN tests that dynamically exploit the trade-off between test accuracy and latency. Compared to the state-of-the-art accelerators, on average, 3DICT improves the CNN test performance per Watt by 13% ∼ 61× and guarantees 9-year endurance under various CNN test accuracy requirements.
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Index Terms
- 3DICT: A Reliable and QoS Capable Mobile Process-In-Memory Architecture for Lookup-based CNNs in 3D XPoint ReRAMs
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