23/4/2014EE, Meyer 861


Look-Ahead Clock Gating

Shmuel Wimer

Bar Ilan University

Clock gating is very useful for reducing the power consumed by digital systems. Three gating methods are known: synthesis-based, data-driven and auto-gated FFs (AGFF). We present a novel method called Look-Ahead Clock Gating (LACG), which combines all the three. LACG computes the clock enabling signals of each FF one cycle ahead of time, based on the present cycle data of those FFs on which it depends. It avoids the tight timing constraints of AGFF and data-driven by allotting a full clock cycle for the computation of the enabling signals and their propagation. A closed-form model characterizing the power saving per FF is presented. The model implies a breakeven curve, dividing the FFs space into two regions of positive and negative gating return on investment. While the majority of the FFs fall in the positive region and hence should be gated, those falling in the negative region should not. Experimentation on industry-scale data showed 23% reduction of the clock power, on top of other gating methods.

Bio: Shmuel Wimer is an Associate Professor with the Engineering Faculty of Bar-Ilan University and a visiting Associate Professor with the EE Dept. of the Technion. He received M.Sc. in mathematics from Tel-Aviv University, and D.Sc. in EE from the Technion. Prior to joining the academia on 2009 he worked for 32 years at the industry for Intel, IBM, National Semiconductors and the Israeli Aerospace Industry. He is interested in optimization of VLSI circuits and systems and in combinatorial optimization.

30/4/2014EE, Meyer TBA


Sampling and Inference Problems for Big Data in the Internet and Beyond

Nick Duffield

Rutgers University

Massive graph datasets are used operationally by providers of internet, social network and search services. Sampling can reduce storage requirements as well as query execution times, while prolonging the useful life of the data for baselining and retrospective analysis. Here, sampling must mediate between the characteristics of the data, the available resources, and the accuracy needs of queries. Inference methods can be used to fuse datasets which individually provide only an incomplete view of the system under study. In this talk we describe some successes in applying these ideas to massive Internet measurements and some potential new applications to inverse problems in urban informatics, and to bioinformatics.

Bio: Nick Duffield joined Rutgers University as a Research Professor in 2013. Previously, he worked at AT &T Labs Research, where he was a Distinguished Member of Technical Staff and an AT &T Fellow, and held faculty positions in Europe. He works on network and data science, particularly the acquisition, analysis and applications of operational network data. He was formerly chair of the IETF Working Group on Packet Sampling, and an associate editor of the IEEE/ACM Transactions on Networking. He is an IEEE Fellow and was a co-recipient of the ACM Sigmetrics Test of Time Award in both 2012 and 2013 for work in network tomography. He was recently TPC Co-Chair of IFIP Performance 2013, a keynote speaker at the 25th International Teletraffic Congress in Shanghai, China, and an invited speaker at the workshop on Big Data in the Mathematical Sciences in Warwick, UK.