We empirically evaluate an undervolting technique, ie, underscaling the circuit supply
voltage below the nominal level, to improve the power-efficiency of Convolutional Neural …

Microprocessor power consumption and dependability are both crucial challenges that
designers have to cope with due to shrinking feature sizes and increasing transistor counts …

Energy efficiency is a known major concern for computing system designers. Significant
effort is devoted to power optimization of modern systems, especially in large-scale …

Modern large-scale computing systems (data centers, supercomputers, cloud and edge
setups and high-end cyber-physical systems) employ heterogeneous architectures that …

Modern large-scale computing systems (data centers, supercomputers, cloud and edge
setups and high-end cyber-physical systems) employ heterogeneous architectures that …

Modern CPUs dynamically scale voltage and frequency for efficiency. However, too low
voltages can result in security-critical errors. Hence, vendors use a generous safety margin …

Branch prediction units are key performance components in modern microprocessors as
they are widely used to address control hazards and minimize misprediction stalls. The …

In this paper, we propose the employment of fast targeted programs (diagnostic micro-
viruses) that aim to stress individually the main hardware components of a multicore CPU …

Deep neural network (DNN) classifiers are powerful tools that drive a broad spectrum of
important applications, from image recognition to autonomous vehicles. Unfortunately …

Advances in reliability research have presented novel techniques for early identification of
upcoming failures (both long-term and short-term) as well as sophisticated isolation and …