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

When a computational task tolerates a relaxation of its specification or when an algorithm
tolerates the effects of noise in its execution, hardware, system software, and programming …

HPC applications use floating point arithmetic operations extensively to solve computational
problems. Mixed-precision computing seeks to use the lowest precision data type that is …

Motivation: In modern it systems, the increasing demand for computational power is tightly
coupled with ever higher energy consumption. Traditionally, energy efficiency research has …

We present Pasado, a technique for synthesizing precise static analyzers for Automatic
Differentiation. Our technique allows one to automatically construct a static analyzer …

With the increasing number of connected devices and the generation of a large amount of
data, efficient methods are required to deal with the complexities of big data processing …

The rapid growth of demanding applications in domains applying multimedia processing
and machine learning has marked a new era for edge and cloud computing. These …

We present a novel, general construction to abstractly interpret higher-order automatic
differentiation (AD). Our construction allows one to instantiate an abstract interpreter for …

The explosive growth of data increases the storage needs, especially within servers, making
DRAM responsible for more than 40% of the total system power. Such a reality has made …

The intrinsic error tolerance of neural network (NN) presents opportunities for approximate
computing techniques to improve the energy efficiency of NN inference. Conventional …