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 …

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

A DRAM device requires periodic refresh operations to preserve data integrity, which incurs
significant power consumption. This paper proposes a new memory architecture to reduce …

DRAM devices require periodic refresh operations to preserve data integrity. Slowing down
the refresh rate can reduce the energy consumption; however, it may cause a loss of data …

Approximate computing is an advanced computational technique that trades the accuracy of
computation results for better utilization of system resources. It has emerged as a new …

Cooperative inference applications have seen considerable potential with distributed deep
neural networks (DDNNs). One use for DDNNs is the classification of 3-D objects from a set …

The rapid proliferation of the Internet of Things and the dramatic resurgence of artificial
intelligence based application workloads have led to immense interest in performing …

The unprecedented progress in computational technologies led to a substantial proliferation
of artificial intelligence applications, notably in the era of big data and IoT devices. In the …

Approximate computing systems improve energy efficiency and computation speed at the
cost of reduced accuracy on system outputs. Existing efforts mainly explore the feasible …