18/3/2015 11:30EE, 1007


Random Graph Models for Random Key Predistribution in WSNs

Prof. Armand M. Makowski

?University of Maryland

I will start with a quick background on wireless sensor networks (WSNs), and some of the security challenges created by their unique features. Random key pre-distribution schemes address some of the difficulties by randomly assigning secure keys to sensor nodes prior to network deployment. The discussion will focus on two very different schemes, namely the scheme of Eschenauer and Gligor, and the random pairwise scheme of Chan et al. Each of these schemes induces a non-standard graph model which differs from the classical binomial random graphs of Erdos and Renyi. In each case the quest for "secure connectivity" (under so-called full visibility) will be explored through zero-one laws for graph connectivity in the corresponding random graph model. Comparison with similar results for Erdos-Renyi graphs will be made. If time permits we will also discuss the partial visibility case. This is joint work with former student Osman Yagan (now at CMU).

Bio: Armand M. Makowski received the Licence en Sciences Mathematiques from the Universite Libre de Bruxelles in 1975, the M.S. degree in Engineering-Systems Science from U.C.L.A. in 1976 and the Ph.D. degree in Applied Mathematics from the University of Kentucky in 1981. In August 1981, he joined the faculty of the Electrical Engineering Department at the University of Maryland at College Park, where he is presently a Professor of Electrical and Computer Engineering. His research interests broadly lie in applying advanced methods from the theory of stochastic processes to the modeling, design and performance evaluation of a variety of engineering systems, with particular emphasis on communication systems and networks. He is an IEEE Fellow.

25/3/2015 11:30CS, Taub 6


Variability SmartBalance: A Sensing-Driven Linux Load Balancer for Energy Efficiency of Heterogeneous MPSoCs

Prof. Alex Nicolau

University of California, Irvine

A short overview of the NSF Variability Expedition will be given, followed by an overview of a particular result: SmartBalance.

Due to increased demand for higher performance and better energy efficiency, MPSoCs are deploying heterogeneous architectures with architecturally differentiated core types. However, the traditional Linux-based operating system is unable to exploit this heterogeneity since existing kernel load balancing and scheduling approaches lack support for aggressively heterogeneous architectural configurations (e.g. beyond two core types). In this paper we present SmartBalance: a sensing-driven closed-loop load balancer for aggressively heterogeneous MPSoCs that performs load balancing using a sense-predict-balance paradigm. SmartBalance can efficiently manage the chip resources while opportunistically exploiting the workload variations and performance-power trade-offs of different core types. When compared to the standard vanilla Linux kernel load balancer, our per-thread and per-core performance-power-aware scheme shows an improvement in energy efficiency (throughput/Watt) of over 50% for benchmarks from the PARSEC benchmark suite executing on a heterogeneous MPSoC with 4 different core types and over 20% w.r.t. state-of-the-art ARM's global task scheduling (GTS) scheme for octa-core big.Little architecture.

Bio: Alex Nicolau received his Ph.D. in Computer Science from Yale University in 1984, and served on the faculty of the computer science department at Cornell University until 1988. That year he joined the University of California, Irvine as an associate professor, where he serves as full professor since 1992 and department chair since 2013.

The author of over 300 conference and journal articles and many books, Alex chaired numerous international conferences (e.g., ACM International Supercomputing Conference (ICS) and Principles and Practice of Parallel Programming) and is editor in chief of the International Journal of Parallel Programming, the oldest journal in that field. He is also an IEEE Fellow.

26/3/2015 13:30CS, Taub 6


Deep learning with NVIDIA GPUs

Jonathan Cohen

NVIDIA Corporation

NVIDIA GPUs are powering a revolution in machine learning. With the rise of deep learning algorithms, in particular deep convolutional neural networks, computers are learning to see, hear, and understand the world around us in ways never before possible. Image recognition and detection systems are getting close to and in some cases surpassing human-level performance. I will talk about deep learning in the context of several new NVIDIA initiatives ranging from hardware platforms, software tools and libraries, and our recently announced DRIVE PX module for autonomous driving.

Bio: Jonathan Cohen is Director of Engineering for NVIDIA's GPU-accelerated deep learning software platform. Before moving to the product side of NVIDIA, Mr. Cohen spent three years as a senior research scientist with NVIDIA Research developing scientific computing and real-time physical simulation applications on NVIDIA's massively parallel GPUs.

1/4/2015 11:30CS, 337


Introducing Intel Software Guard Extensions (Intel SGX)

Ittai Anati


Intel Software Guard Extensions (Intel SGX) is an extension to Intel Architecture designed to increase the security of software. In this approach, rather than attempting to identify and isolate all the malware on the platform, legitimate software can be sealed inside an enclave and protected from attack by malware, irrespective of the its privilege level. In the talk I will touch on the building blocks of security, describe the basics of Intel SGX, and show how the components, combined, provide a holistic secure solution.

Bio: Ittai Anati is a Senior Principal Engineer at Intel Corporation, focusing mainly on topics related to CPU and system security at Intel's Israel Design Center (IDC) in Haifa. Ittai has a B.Sc. in Electrical Engineering from the Technion, Israel Institute of Technology.