Summary of A Converting Autoencoder Toward Low-latency and Energy-efficient Dnn Inference at the Edge, by Hasanul Mahmud et al.
A Converting Autoencoder Toward Low-latency and Energy-efficient DNN Inference at the Edge
by Hasanul Mahmud, Peng Kang, Kevin Desai, Palden Lama, Sushil Prasad
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach for deep neural network (DNN) inference on resource-constrained edge devices, focusing on reducing inference time and energy usage while maintaining prediction accuracy. The authors introduce CBNet, a low-latency and energy-efficient DNN inference framework that utilizes a “converting” autoencoder to transform hard images into easy ones, which are then processed by a lightweight DNN for inference. This approach improves upon recent work such as early-exiting frameworks and DNN partitioning algorithms. CBNet achieves significant improvements in inference latency and energy usage compared to competing techniques, with up to 4.8x speedup and 79% reduction in energy usage on popular image-classification datasets like CIFAR-10, ImageNet, and Tiny ImageNet. The authors demonstrate the effectiveness of CBNet using a Raspberry Pi 4, a Google Cloud instance, and an instance with Nvidia Tesla K80 GPU. The paper’s main contributions include the introduction of the “converting” autoencoder and the CBNet framework, which shows promise in addressing the challenges of DNN inference on edge devices. The authors’ experimental results provide valuable insights into the performance of CBNet compared to state-of-the-art techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers have developed a new way to make computer vision models run faster and use less energy on small computers like those found in smartphones or smart home devices. They designed a special model that can quickly turn tricky images into easy ones, which then require much less computing power to analyze. This approach is better than previous methods because it works well even when processing many difficult images at once. The team tested their new model on three different image classification tasks and found that it was able to complete the tasks up to 4.8 times faster and use up to 79% less energy compared to other state-of-the-art models. They also showed that their model works well on devices like a Raspberry Pi computer, as well as in cloud computing environments. |
Keywords
* Artificial intelligence * Autoencoder * Image classification * Inference * Neural network
