Massoud Pedram

TL;DR: In this article, a pre-defined sparse 2D kernels that have support sets that repeat periodically within and across filters are introduced to improve the energy efficiency of deep convolutional neural networks.

TL;DR: A complete matrix package based on FEVBDDs is presented and the introduction of multiplicative edge weights allows us to directly represent the so-called complement edges which are used in OBDDs, thus providing a one to one mapping of all O BDDs to FEV BDDs.

TL;DR: The optimal control algorithm for the HEES system is developed, which aims at minimization of the total electricity cost over a billing period under a general electricity energy price function and has polynomial time complexity.

TL;DR: A canonical and compact data structure, called Edge Valued Binary Decision Diagrams (EVBDD), for representing and manipulating pseudo Boolean functions (PBF), and an extension of EVBDDs which associates both an additive and a multiplicative weight with the true edges of the function graph.

TL;DR: This paper introduces the hierarchical modeling of Markov chains as a flexible framework for capturing not only complex spatiotemporal correlations, but also dynamic changes in the sequence characteristics and introduces and characterize a family of variable-order dynamic Markov models which provide an effective way for accurate modeling of external input sequences that affect the behavior of finite state machines.