autoAx: An Automatic Design Space Exploration and Circuit Building Methodology utilizing Libraries of Approximate Components

Approximate computing is an emerging paradigm for developing highly energy-efficient computing systems such as various accelerators. In the literature, many libraries of elementary approximate circuits have already been proposed to simplify the design process of approximate accelerators. Because these libraries contain from tens to thousands of approximate implementations for a single arithmetic operation it is intractable to find an optimal combination of approximate circuits in the library even for an application consisting of a few operations. An open problem is "how to effectively combine circuits from these libraries to construct complex approximate accelerators". This paper proposes a novel methodology for searching, selecting and combining the most suitable approximate circuits from a set of available libraries to generate an approximate accelerator for a given application. To enable fast design space generation and exploration, the methodology utilizes machine learning techniques to create computational models estimating the overall quality of processing and hardware cost without performing full synthesis at the accelerator level. Using the methodology, we construct hundreds of approximate accelerators (for a Sobel edge detector) showing different but relevant tradeoffs between the quality of processing and hardware cost and identify a corresponding Pareto-frontier. Furthermore, when searching for approximate implementations of a generic Gaussian filter consisting of 17 arithmetic operations, the proposed approach allows us to identify approximately $10^3$ highly important implementations from $10^{23}$ possible solutions in a few hours, while the exhaustive search would take four months on a high-end processor.Comment: Accepted for publication at the Design Automation Conference 2019 (DAC'19), Las Vegas, Nevada, US

Reversible implementation of a disrete linear transformation

Discrete linear transformations form important steps in processing information. Many such transformations are injective and therefore are prime candidates for a physically reversible implementation into hardware. We present here the first steps towards a reversible digital implementation of two different integer transformations on four inputs: The Haar wavelet and the H.264 transform

"Second-Best" Adjustments to Externality Estimates in Electricity Planning with Competition

A number of state public utility commissions are using "social costing" methods to consider externalities in electricity resource planning. The most comprehensive and formal method is the use of monetary place-holders in the financial evaluation of new investments and potentially in system dispatch to reflect quantitative estimates of externality values. This approach necessarily must take existing environmental and social regulation as given. Furthermore, regulated utilities face increasing competition from electricity generators outside their service territory who may not be affected by social costing. The lack of universal and uniform social costing places PUC actions soundly in the realm of "second-best policy" and they may have unintended consequences that should be anticipated by regulators. This paper addresses two prominent possibilities: the potential substitution of unregulated supplies of energy services in place of electricity generated by the regulated utility, and the effect social costing may have on the relationship between the regulated price and marginal cost. These issues are considered within a normative model of social welfare maximization, which is applied to three representative hypothetical utility case studies to calibrate a second-best optimal adder to correct for externalities in electricity planning.