As computing becomes increasingly pervasive in our daily life, it is generally recognized that energy efficiency will be one of the key design considerations for any future computing scheme. Consequently, significant research is currently ongoing on exploring new physics, material systems and system level designs to improve energy efficiency. In this talk, I shall discuss some of our recent progresses in this regard. Specifically, the physics of ordered and correlated systems allow for fundamental improvement of the energy efficiency when a transition happens between two distinguishable states. Our recent experiments show that this theoretical promise can indeed be realized in transistors and spintronic memory devices. The resulting gain in energy efficiency can easily exceed orders of magnitude.
Sayeef Salahuddin is an associate professor of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His research interests are in the transport physics of nano structures currently focusing on novel electronic and spintronic devices for low power logic... read more »
In 2009 when the Obama-Biden ticket was inaugurated into office, they set out to accomplish the following aspirational goals:
And in the 2011 State of the Union address, the President put forth a goal to reach 80% clean electricity by 2035. The Office of the Under Secretary for Energy at the US Department of Energy put together a plan to realize these goals, along with the cost of attaining 80% clean electricity by 2035. In this talk, I will summarize this plan, called the "Strategic Technology Energy Plan (STEP)" as well highlight the unique role of run-of-river and pumped storage hydropower in STEP.
Prior to founding Enduring Hydro in 2011, Dr. Johnson served as undersecretary at the Department of Energy in Washington, D.C. from May 2009 until October 2010. Before her appointment as undersecretary, Dr. Johnson was provost and senior vice president for Academic Affairs at the Johns Hopkins... read more »
We consider the problem of decomposing a signal into a linear combination of features, each a continuously translated version of one of a small set of elementary features. Although these constituents are drawn from a continuous family, most current signal decomposition methods rely on a finite dictionary of discrete examples selected from this family (e.g.,a set of shifted copies of a set of basic waveforms), and apply sparse optimization methods to select and solve for the relevant coefficients. Here, we generate a dictionary that includes auxilliary interpolation functions that approximate translates of features via adjustment of their coefficients. We formulate a constrained convex optimization problem, in which the full set of dictionary coefficients represent a linear approximation of the signal, the auxiliary coefficients are constrained so as to onlyrepresent translated features, and sparsity is imposed on the non-auxiliary coefficients using an L1 penalty. The well-known basis pursuit denoising (BP) method may be seen as a special case, in which the auxiliary interpolation functions are omitted, and we thus refer to our methodology ascontinuous basis pursuit (CBP). We develop two implementations of CBP for a onedimensional translationinvariant source, one using a first-order Taylor approximation, and another using a form of trigonometric spline. We examine the tradeoff between sparsity and signal reconstruction accuracy in these methods, demonstrating empirically that trigonometric CBP substantially outperforms Taylor CBP, which in turn offers substantial gains over ordinary BP. In addition, the CBP bases can generally achieve equally good or better approximations with much coarser sampling than BP, leading to a reduction in dictionary dimensionality
Dr. Simoncelli is a Professor of Neural Science, Mathematics, and Psychology at New York University. He began his higher education as a physics major at Harvard, studied mathematics at Cambridge University for a year and a half on a Knox Fellowship , and earned a doctorate in electrical... read more »