Nicholas D. Lane
ABSTRACT
Recent breakthroughs in deep learning are enabling new ways of interpreting and analyzing sensor measurements to extract high-level information needed by mobile and IoT apps. Thus for improving usability, it is essential that the deep models are embedded in next generation mobile and IoT apps, where inference tasks are often challenging due to high measurement noise. However, deep learning-based models are yet to become mainstream on embedded platforms, where device resources, e.g., memory, computation and energy, are limited. In this demonstration, we present DeepX, a software accelerator that allows running deep neural network (DNN) and deep convolutional neural network (CNN) efficiently on resource constrained mobile platforms. DeepX significantly lowers device resource requirements during deep model- based inferencing, which currently act as the severe bottleneck to wide-scale mobile adoption.
- Y. Bengio et al., "Deep learning," 2015, MIT Press.Google Scholar
- N. D. Lane et al., "Can Deep Learning Revolutionize Mobile Sensing?," in HotMobile, 2015. Google ScholarDigital Library
- N. D. Lane et al., "DeepX: A software accelerator for low-power deep learning inference on mobile devices," in IPSN, 2016. Google ScholarDigital Library
- O. Russakovsky et al., "Imagenet large scale visual recognition challenge," IJCV, 2015. Google ScholarDigital Library
Index Terms
- Demo: Accelerated Deep Learning Inference for Embedded and Wearable Devices using DeepX
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