As the end of Dennard scaling looms, both the semiconductor industry and the research community are exploring for innovative solutions that allow energy efficiency and performance to continue to scale. Approximation ...

This paper describes ExpAX, a framework for automating approximate programming based on programmer-specified error expectations. Three components constitute ExpAX: (1) a programming model based on a new kind of program ...

Design and reuse of approximate hardware components—digital circuits that may produce inaccurate results—can potentially lead to significant performance and energy improvements. Many emerging error-resilient applications ...

A growing number of commercial and enterprise systems increasingly rely on compute-intensive machine learning algorithms. While the demand for these compute-intensive applications is growing, the performance benefits from ...

General-purpose computing on graphics processing units (GPGPU) accelerates the execution of diverse classes of applications, such as recognition, gaming, data analytics, weather prediction, and multimedia. Many of these ...

This paper aims to tackle two fundamental memory bottle-necks: limited off-chip bandwidth (bandwidth wall) and long access latency (memory wall). To achieve this goal, our approach exploits the inherent error resilience ...

We present ExpAX, a framework for automating approximate programming. ExpAX consists of these three components: (1) a programming model based on a new kind of program specification, which we refer to as error ...