At ICSE'2013, there was the first session ever dedicated to automatic program repair. In this
session, Kim et al. presented PAR, a novel template-based approach for fixing Java bugs …

The challenging deployment of compute-intensive applications from domains such as
Artificial Intelligence (AI) and Digital Signal Processing (DSP), forces the community of …

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 …

As one of the most promising energy-efficient computing paradigms, approximate computing
has gained a lot of research attention in the past few years. This paper presents a survey of …

This paper describes a learning-based approach to the acceleration of approximate
programs. We describe the Parrot transformation, a program transformation that selects and …

Energy is increasingly a first-order concern in computer systems. Exploiting energy-accuracy
trade-offs is an attractive choice in applications that can tolerate inaccuracies. Recent work …

Transiently powered computing devices such as RFID tags, kinetic energy harvesters, and
smart cards typically rely on programs that complete a task under tight time constraints …

Deep Convolutional Neural Networks (CNNs) perform billions of operations for classifying a
single input. To reduce these computations, this paper offers a solution that leverages a …

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, where computation accuracy is traded off for better performance or
higher data throughput, is one solution that can help data processing keep pace with the …