Approximate computing trades off computation quality with effort expended, and as rising
performance demands confront plateauing resource budgets, approximate computing has …

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

Computer clusters with the shared-nothing architecture are the major computing platforms
for big data processing and analysis. In cluster computing, data partitioning and sampling …

Computing systems are conventionally designed to operate as accurately as possible.
However, this trend faces severe technology challenges, such as power consumption, circuit …

Brain-inspired hyperdimensional (HD) computing emulates cognition tasks by computing
with hypervectors as an alternative to computing with numbers. At its very core, HD …

This paper presents in detail our fast semistochastic heat-bath configuration interaction
(SHCI) method for solving the many-body Schrödinger equation. We identify and eliminate …

Online analytical processing (OLAP) is a core functionality in database systems. The
performance of OLAP is crucial to make online decisions in many applications. However, it is …

Mobile and IoT scenarios increasingly involve interactive and computation intensive
contextual recognition. Existing optimizations typically resort to computation offloading or …

The threshold theorem is a seminal result in the field of quantum computing asserting that
arbitrarily long quantum computations can be performed on a faulty quantum computer …

With the ever-increasing volume of data, alternative strategies are required to divide big data
into statistically consistent data blocks that can be used directly as representative samples of …