24,143 research outputs found
EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices
In recent years, advances in deep learning have resulted in unprecedented
leaps in diverse tasks spanning from speech and object recognition to context
awareness and health monitoring. As a result, an increasing number of
AI-enabled applications are being developed targeting ubiquitous and mobile
devices. While deep neural networks (DNNs) are getting bigger and more complex,
they also impose a heavy computational and energy burden on the host devices,
which has led to the integration of various specialized processors in commodity
devices. Given the broad range of competing DNN architectures and the
heterogeneity of the target hardware, there is an emerging need to understand
the compatibility between DNN-platform pairs and the expected performance
benefits on each platform. This work attempts to demystify this landscape by
systematically evaluating a collection of state-of-the-art DNNs on a wide
variety of commodity devices. In this respect, we identify potential
bottlenecks in each architecture and provide important guidelines that can
assist the community in the co-design of more efficient DNNs and accelerators.Comment: Accepted at MobiSys 2019: 3rd International Workshop on Embedded and
Mobile Deep Learning (EMDL), 201
Enabling Social Applications via Decentralized Social Data Management
An unprecedented information wealth produced by online social networks,
further augmented by location/collocation data, is currently fragmented across
different proprietary services. Combined, it can accurately represent the
social world and enable novel socially-aware applications. We present
Prometheus, a socially-aware peer-to-peer service that collects social
information from multiple sources into a multigraph managed in a decentralized
fashion on user-contributed nodes, and exposes it through an interface
implementing non-trivial social inferences while complying with user-defined
access policies. Simulations and experiments on PlanetLab with emulated
application workloads show the system exhibits good end-to-end response time,
low communication overhead and resilience to malicious attacks.Comment: 27 pages, single ACM column, 9 figures, accepted in Special Issue of
Foundations of Social Computing, ACM Transactions on Internet Technolog
Quantifying the latency benefits of near-edge and in-network FPGA acceleration
Transmitting data to cloud datacenters in distributed IoT applications introduces significant communication latency, but is often the only feasible solution when source nodes are computationally limited. To address latency concerns, cloudlets, in-network computing, and more capable edge nodes are all being explored as a way of moving processing capability towards the edge of the network. Hardware acceleration using Field Programmable Gate Arrays (FPGAs) is also seeing increased interest due to reduced computation latency and improved efficiency. This paper evaluates the the implications of these offloading approaches using a case study neural network based image classification application, quantifying both the computation and communication latency resulting from different platform choices. We consider communication latency including the ingestion of packets for processing on the target platform, showing that this varies significantly with the choice of platform. We demonstrate that emerging in-network accelerator approaches offer much improved and predictable performance as well as better scaling to support multiple data sources
Plausible Mobility: Inferring Movement from Contacts
We address the difficult question of inferring plausible node mobility based
only on information from wireless contact traces. Working with mobility
information allows richer protocol simulations, particularly in dense networks,
but requires complex set-ups to measure, whereas contact information is easier
to measure but only allows for simplistic simulation models. In a contact trace
a lot of node movement information is irretrievably lost so the original
positions and velocities are in general out of reach. We propose a fast
heuristic algorithm, inspired by dynamic force-based graph drawing, capable of
inferring a plausible movement from any contact trace, and evaluate it on both
synthetic and real-life contact traces. Our results reveal that (i) the quality
of the inferred mobility is directly linked to the precision of the measured
contact trace, and (ii) the simple addition of appropriate anticipation forces
between nodes leads to an accurate inferred mobility.Comment: 8 pages, 8 figures, 1 tabl
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