Concrete: A Per-layer Configurable Framework for Evaluating DNN with Approximate Operators

Zheyu, Liu; Li, Guihong; Qiao, Fei; Wei, Qi; Jin, Ping; Liu, Xinjun; Yang, Huazhong

doi:10.1109/icassp.2019.8682883

Cited by 5 publications

(1 citation statement)

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“…Specifically, for DNNs, as a modern state-of-the-art application, it has been shown that they are inherently resilient to errors. Thanks to this feature, a broad set of research studies have employed approximate computing techniques to DNNs [124], [125]. By this way, the energy and performance of such systems can be effectively improved by running in the presence of errors without sacrificing their classification accuracy.…”

Section: ) Power/energy-aware Interconnects For Embeddedmentioning

confidence: 99%

An Overview of Efficient Interconnection Networks for Deep Neural Network Accelerators

Nabavinejad

Baharloo

Chen

et al. 2020

IEEE J. Emerg. Sel. Topics Circuits Syst.

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Deep Neural Networks (DNNs) have shown significant advantages in many domains, such as pattern recognition, prediction, and control optimization. The edge computing demand in the Internet-of-Things (IoTs) era has motivated many kinds of computing platforms to accelerate DNN operations. However, due to the massive parallel processing, the performance of the current large-scale artificial neural network is often limited by the huge communication overheads and storage requirements. As a result, efficient interconnection and data movement mechanisms for future on-chip artificial intelligence (AI) accelerators are worthy of study. Currently, a large body of research aims to find an efficient on-chip interconnection to achieve low-power and high-bandwidth DNN computing. This paper provides a comprehensive investigation of the recent advances in efficient on-chip interconnection and design methodology of the DNN accelerator design. First, we provide an overview of the different interconnection methods on the DNN accelerator. Then, the interconnection methods on the non-ASIC DNN accelerator will be discussed. On the other hand, with the flexible interconnection, the DNN accelerator can support different computing flow, which increases the computing flexibility. With this motivation, reconfigurable DNN computing with flexible on-chip interconnection will be investigated in this paper. Finally, we investigate the emerging interconnection technologies (e.g., in/near-memory processing) for the DNN accelerator design. This paper systematically investigates the interconnection networks in modern DNN accelerator designs. With this article, the readers are able to: 1) understand

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Section: ) Power/energy-aware Interconnects For Embeddedmentioning

confidence: 99%