No abstract available.

No abstract available.

Wearable devices with built-in cameras present interesting opportunities for users to capture various aspects of their daily life and are potentially also useful in supporting users with low vision in their everyday tasks. However, state-of-the-art image wearables available in the market are limited to capturing images periodically and do not provide any real-time analysis of the data that might be useful for the wearers. In this paper, we present DeepEye - a match-box sized wearable camera that is capable of running multiple cloud-scale deep learn- ing models locally on the device, thereby enabling rich analysis of the captured images in near real-time without offloading them to the cloud. DeepEye is powered by a commodity wearable processor (Snapdragon 410) which ensures its wearable form factor. The software architecture for DeepEye addresses a key limitation with executing multiple deep learning models on constrained hardware, that is their limited runtime memory. We propose a novel inference software pipeline that targets the local execution of multiple deep vision models (specifically, CNNs) by interleaving the execution of computation-heavy convolutional layers with the loading of memory-heavy fully-connected layers. Beyond this core idea, the execution framework incorporates: a memory caching scheme and a selective use of model compression techniques that further minimizes memory bottlenecks. Through a series of experiments, we show that our execution framework outperforms the baseline approaches significantly in terms of inference latency, memory requirements and energy consumption.

Industrial events and exhibitions play a powerful role in creating social relations amongst individuals and firms, enabling them to expand their social network so to acquire resources. However, often these events impose a spatial structure which impacts encounter opportunities. In this paper, we study the impact that the spatial configuration has on the formation of network relations. We designed, developed and deployed a Wi-Fi analytics solution comprising of wearable Wi-Fi badges and gateways in a large scale industrial exhibition event to study the spatio-temporal trajectories of the 2.5K+ attendees including two special groups: 34 investors and 27 entrepreneurs. Our results suggest that certain zones with designated functionalities play a key role in forming social ties across attendees and the different behavioural properties of investors and entrepreneurs can be explained through a spatial lens. Based on our findings we offer three concrete recommendations for future organisers of networking events.

Breakthroughs from the field of deep learning are radically changing how sensor data are interpreted to extract the high-level information needed by mobile apps. It is critical that the gains in inference accuracy that deep models afford become embedded in future generations of mobile apps. In this work, we present the design and implementation of DeepX, a software accelerator for deep learning execution. DeepX signif- icantly lowers the device resources (viz. memory, computation, energy) required by deep learning that currently act as a severe bottleneck to mobile adoption. The foundation of DeepX is a pair of resource control algorithms, designed for the inference stage of deep learning, that: (1) decompose monolithic deep model network architectures into unit- blocks of various types, that are then more efficiently executed by heterogeneous local device processors (e.g., GPUs, CPUs); and (2), perform principled resource scaling that adjusts the architecture of deep models to shape the overhead each unit-blocks introduces. Experiments show, DeepX can allow even large-scale deep learning models to execute efficently on modern mobile processors and significantly outperform existing solutions, such as cloud-based offloading.

Deep learning has revolutionized the way sensor measurements are interpreted and application of deep learning has seen a great leap in inference accuracies in a number of fields. However, the significant requirement for memory and computational power has hindered the wide scale adoption of these novel computational techniques on resource constrained wearable and mobile platforms. In this demonstration we present DeepX, a software accelerator for efficiently running deep neural networks and convolutional neural networks on resource constrained embedded platforms, e.g., Nvidia Tegra K1 and Qualcomm Snapdragon 400.