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A converting autoencoder Toward Low-latency and Energy-efficient DNN Inference at the Edge

A Converting Autoencoder Toward Low-latency and Energy-effic...

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1st International Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Mahmud, Hasanul Kang, Peng Desai, Kevin Lama, Palden Prasad, Sushil K. Univ Texas San Antonio Dept Comp Sci San Antonio TX 78249 USA

ISBN: (纸本)9798350364613;9798350364606

Reducing inference time and energy usage while maintaining prediction accuracy has become a significant concern for deep neural networks (DNN) inference on resourcecon-strained edge devices. To address this problem, we propose a novel approach based on "converting" autoencoder and lightweight DNNs. This improves upon recent work such as early-exiting framework and DNN partitioning. Early-exiting frameworks spend different amounts of computation power for different input data depending upon their complexity. However, they can be inefficient in real-world scenarios that deal with many hard image samples. On the other hand, DNN partitioning algorithms that utilize the computation power of both the cloud and edge devices can be affected by network delays and intermittent connections between the cloud and the edge. We present CBNet, a low-latency and energy-efficient DNN inference framework tailored for edge devices. It utilizes a "converting" autoencoder to efficiently transform hard images into easy ones, which are subsequently processed by a lightweight DNN for inference. To the best of our knowledge, such autoencoder has not been proposed earlier. Our experimental results using three popular image-classification datasets on a Raspberry Pi 4, a Google Cloud instance, and an instance with Nvidia Tesla K80 GPU show that CBNet achieves up to 4.8 x speedup in inference latency and 79% reduction in energy usage compared to competing techniques while maintaining similar or higher accuracy.

关键词： Energy-efficiency Deep Neural Networks Edge Computing Early-exit DNNs converting autoencoder

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CAE-Net: Enhanced converting autoencoder based Framework for Low-latency Energy-efficient DNN with SLO-constraints

CAE-Net: Enhanced Converting Autoencoder based Framework for...

引用

IEEE Cloud Summit

作者： Mahmud, Hasanul Kang, Peng Lama, Palden Desai, Kevin Prasad, Sushil K. Univ Texas San Antonio Dept Comp Sci San Antonio TX 78249 USA

ISBN: (纸本)9798350370072;9798350370065

As deep neural networks (DNNs) continue to be used on resource-limited edge devices with low latency requirements for interactive applications, there is a growing need to reduce inference time and energy consumption while maintaining acceptable prediction accuracy. In response, we introduce a novel framework, CAE-Net, for designing and training lightweight and energyefficient deep neural networks (DNNs) for image classification on edge devices. The proposed framework consists of two parts: (1) a new Enhanced converting autoencoder that employs entropy-based intraclass clustering to learn the key image features by transforming the hard images into easy representative images, and (2) a composite lightweight CAE-Net classifier employing the pre-trained encoder of the converting autoencoder followed by a few classification layers from a baseline DNN trained using knowledge transfer. Unlike many state-of-the-art models, our experimental results using popular image-classification datasets, MNIST and CIFAR10 demonstrate that CAE-Net can satisfy the inference latency target of 10-20ms on Raspberry Pi and 5-10 ms on Nvidia Jetson Nano. Compared with the competing models meeting the SLO targets, CAE-Net achieves over 4-fold energy reduction and inferencing latency speedups on the CIFAR-10 dataset compared to AlexNet and its pruned/distilled variants and other DNNs on Raspberry Pi and about 6-fold on Jetson Nano while maintaining similar or higher accuracy.

关键词： converting autoencoder Low latency Energy efficiency Edge devices DNN compression

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