3DICT: A Reliable and QoS Capable Mobile Process-In-Memory Architecture for Lookup-based CNNs in 3D XPoint ReRAMs
3DICT: A Reliable and QoS Capable Mobile Process-In-Memory Architecture for Lookup-based CNNs in 3D XPoint ReRAMs
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DOI：
10.1145/3240765.3240767
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发表时间：
2018-11
期刊：
影响因子：
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通讯作者：
Qian Lou;Wujie Wen;Lei Jiang
中科院分区：
文献类型：
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作者：
Qian Lou;Wujie Wen;Lei Jiang
由于计算资源有限且功耗预算低，在移动设备中部署具有丰富参数的计算密集型卷积神经网络（CNN）极具挑战性。尽管之前的工作通过极大地牺牲测试精度来构建快速且节能的 CNN 加速器，但移动设备必须保证关键应用的高 CNN 测试精度，例如通过人脸识别解锁手机。在本文中，我们提出了一种基于 3D XPoint ReRAM 的内存处理架构 3DICT，通过基于查找的 CNN 测试动态地利用测试精度和延迟之间的权衡，为具有不同优先级的应用程序提供各种测试精度。与最先进的加速器相比，3DICT 平均将每瓦 CNN 测试性能提高 13%∼61 倍，并保证在各种 CNN 测试精度要求下的 9 年耐用性。
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.