31,486 research outputs found
QoE in Pull Based P2P-TV Systems: Overlay Topology Design Tradeoff
Abstract—This paper presents a systematic performance anal-ysis of pull P2P video streaming systems for live applications, providing guidelines for the design of the overlay topology and the chunk scheduling algorithm. The contribution of the paper is threefold: 1) we propose a realistic simulative model of the system that represents the effects of access bandwidth heterogeneity, latencies, peculiar characteristics of the video, while still guaranteeing good scalability properties; 2) we propose a new latency/bandwidth-aware overlay topology design strategy that improves application layer performance while reducing the underlying transport network stress; 3) we investigate the impact of chunk scheduling algorithms that explicitly exploit properties of encoded video. Results show that our proposal jointly improves the actual Quality of Experience of users and reduces the cost the transport network has to support. I
Performance of VoIP with DCCP for satellite links
We present experimental results for the performance of selected voice codecs using the Datagram Congestion Control Protocol (DCCP) with TCP-Friendly Rate Control (TFRC) congestion control mechanism over a satellite link. We evaluate the performance of both constant and variable data rate speech codecs (G.729, G.711 and Speex) for a number of simultaneous calls, using the ITU E-model and identify problem areas and potential for improvement. Our experiments are done on a commercial satellite service using a data stream generated by a VoIP application,
configured with selected voice codecs and using the DCCP/CCID4 Linux implementation. We analyse the sources of packet losses which are a main contributor to reduced
voice quality when using CCID4 and additionally analyse the
effect of jitter which is one of the crucial parameters contributing to VoIP quality and has, to the best of our knowledge, not been considered previously in the published DCCP performance results. We propose modifications to the CCID4 algorithm and demonstrate how these improve the VoIP performance, without the need for additional link information other than what is already monitored by CCID4 (which is the case for Quick-Start). We also demonstrate the fairness of the proposed modifications to other flows. We identify the additional benefit of DCCP when used in VoIP admission control mechanisms and draw conclusions
about the advantages and disadvantages of the proposed
DCCP/ CCID4 congestion control mechanism for use with VoIP
applications
EIE: Efficient Inference Engine on Compressed Deep Neural Network
State-of-the-art deep neural networks (DNNs) have hundreds of millions of
connections and are both computationally and memory intensive, making them
difficult to deploy on embedded systems with limited hardware resources and
power budgets. While custom hardware helps the computation, fetching weights
from DRAM is two orders of magnitude more expensive than ALU operations, and
dominates the required power.
Previously proposed 'Deep Compression' makes it possible to fit large DNNs
(AlexNet and VGGNet) fully in on-chip SRAM. This compression is achieved by
pruning the redundant connections and having multiple connections share the
same weight. We propose an energy efficient inference engine (EIE) that
performs inference on this compressed network model and accelerates the
resulting sparse matrix-vector multiplication with weight sharing. Going from
DRAM to SRAM gives EIE 120x energy saving; Exploiting sparsity saves 10x;
Weight sharing gives 8x; Skipping zero activations from ReLU saves another 3x.
Evaluated on nine DNN benchmarks, EIE is 189x and 13x faster when compared to
CPU and GPU implementations of the same DNN without compression. EIE has a
processing power of 102GOPS/s working directly on a compressed network,
corresponding to 3TOPS/s on an uncompressed network, and processes FC layers of
AlexNet at 1.88x10^4 frames/sec with a power dissipation of only 600mW. It is
24,000x and 3,400x more energy efficient than a CPU and GPU respectively.
Compared with DaDianNao, EIE has 2.9x, 19x and 3x better throughput, energy
efficiency and area efficiency.Comment: External Links: TheNextPlatform: http://goo.gl/f7qX0L ; O'Reilly:
https://goo.gl/Id1HNT ; Hacker News: https://goo.gl/KM72SV ; Embedded-vision:
http://goo.gl/joQNg8 ; Talk at NVIDIA GTC'16: http://goo.gl/6wJYvn ; Talk at
Embedded Vision Summit: https://goo.gl/7abFNe ; Talk at Stanford University:
https://goo.gl/6lwuer. Published as a conference paper in ISCA 201
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