Name
Title | ||
|---|---|---|
3DICT: a reliable and QoS capable mobile process-in-memory architecture for lookup-based CNNs in 3D XPoint ReRAMs |
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% ~ 61X and guarantees 9-year endurance under various CNN test accuracy requirements.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3240765.3240767 | ICCAD-IEEE ACM International Conference on Computer-Aided Design |
Keywords | Field | DocType |
3D vertical ReRAM,Lookup -based CNN,process-in-memory | 3D XPoint,Computer architecture,Convolutional code,Convolutional neural network,Computer science,Quality of service,Exploit,Real-time computing,Mobile device,Performance per watt,Memory architecture | Conference |
ISSN | ISBN | Citations |
1933-7760 | 978-1-4503-5950-4 | 2 |
PageRank | References | Authors |
0.35 | 19 | 3 |