Genetic improvement (GI) uses automated search to find improved versions of existing
software. We present a comprehensive survey of this nascent field of research with a focus …

Approximate computing is gaining traction as a computing paradigm for data analytics and
cognitive applications that aim to extract deep insight from vast quantities of data. In this …

In this paper we discuss several forms of spatiotemporal convolutions for video analysis and
study their effects on action recognition. Our motivation stems from the observation that 2D …

Program autotuning has been shown to achieve better or more portable performance in a
number of domains. However, autotuners themselves are rarely portable between projects …

Energy has become a first-class design constraint in computer systems. Memory is a
significant contributor to total system power. This paper introduces Flikker, an application …

This paper describes some of the challenges and opportunities when deploying static and
dynamic analysis at scale, drawing on the authors' experience with the Infer and Sapienz …

Memories today expose an all-or-nothing correctness model that incurs significant costs in
performance, energy, area, and design complexity. But not all applications need high …

Battery-free, energy-harvesting devices operate using energy collected exclusively from
their environment. Energy-harvesting devices allow maintenance-free deployment in …

Many modern computations (such as video and audio encoders, Monte Carlo simulations,
and machine learning algorithms) are designed to trade off accuracy in return for increased …

Approximate computing has emerged as a new design paradigm that exploits the inherent
error resilience of a wide range of application domains by allowing hardware …