Nowadays, neural networks have become the main part of machine learning tasks. To get high-performing networks, we require high computational resources that are far beyond mobile device capabilities. Shallow neural networks alone generally have poorer predictive accuracy compared to state-of-the-art deep neural networks, but they allow real-time prediction on edge devices, which makes them more suitable for edge computing. Towards applying those heavy models on mobile devices, the works we have done here are tailored to decrease the number of operations of models while keeping the original performance.
With significant increases in wireless link capacity, edge devices are more connected than ever, which makes possible forming artificial neural network (ANN) federations on the connected edge devices. Partition is the key to the success of distributed ANN inference while unsolved because of the nclear knowledge representation in most of the ANN models. We propose a novel partition approach (TeamNet) based on the psychologically-plausible competitive and selective learning schemes while evaluating its performance carefully with thorough comparisons to other existing distributed machine learning approaches. Our experiments demonstrate that TeamNet with sockets and transmission control protocol (TCP) significantly outperforms sophisticated message passing interface (MPI) approaches and the state-of-the-art mixture of experts (MoE) approaches. The response time of ANN inference is shortened by as much as 53% without compromising predictive accuracy. TeamNet is promising for having distributed ANN inference on connected edge devices and forming edge intelligence for future applications.here
The success of deep neural networks (DNN) in machine perception applications such as image classification and speechrecognition comes at the cost of high computation and storage complexity. Inference of uncompressed large scale DNN models canonly run in the cloud with extra communication latency back and forth between cloud and end devices, while compressed DNN modelsachieve real-time inference on end devices at the price of lower predictive accuracy. In order to have the best of both worlds (latencyand accuracy), we propose CacheNet, a model caching framework. CacheNet caches low-complexity models on end devices andhigh-complexity (or full) models on edge or cloud servers. By exploiting temporal locality in streaming data, high cache hit andconsequently shorter latency can be achieved with no or only marginal decrease in prediction accuracy. Experiments on CIFAR-10 andFVG have shown CacheNet is58−217%faster than baseline approaches that run inference tasks on end devices or edge servers alone.
In surveillance and search and rescue applications, it is important to perform multi-target tracking (MOT) in real-time on low-end devices. Today’s MOT solutions employ deep neural networks, which tend to have high computation complexity. Recognizing the effects of frame sizes on tracking performance, we propose DeepScale, a model agnostic frame size selection approach that operates on top of existing fully convolutional network-based trackers to accelerate tracking throughput. In the training stage, we incorporate detectability scores into a one-shot tracker architecture so that DeepScale can learn representation estimations for different frame sizes in a self-supervised manner. During inference, it can adapt frame sizes according to the complexity of visual contents based on user-controlled parameters. Extensive experiments and benchmark tests on MOT datasets demonstrate the effectiveness and flexibility of DeepScale. Compared to a state-of-the-art tracker, DeepScale++, a variant of DeepScale achieves 1.57X accelerated with only moderate degradation (~ 2.3%) in tracking accuracy on the MOT15 dataset in one configuration.
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