Memory isolation is critical for system reliability, security, and safety. Unfortunately, read
disturbance can break memory isolation in modern DRAM chips. For example, RowHammer …

Machine learning is among the greatest advancements in computer science and
engineering and is today used to classify or detect objects, a key feature in autonomous …

Deep neural network (DNN) training workloads are increasingly susceptible to hardware
failures in datacenters. For example, Google experienced" mysterious, difficult to identify …

The emergence of heterogeneous FPGA-based SoCs and their growing complexity fueled
by the introduction of various accelerators bring the reliability aspect of these systems to the …

We propose AVGI, a new Statistical Fault Injection (SFI)-based methodology, which delivers
orders of magnitude faster assessment of the Architectural Vulnerability Factor (AVF) of a …

Today more than ever before, academia, manufacturers, and hyperscalers acknowledge the
major challenge of silent data corruptions (SDCs) and aim on solutions to minimize its …

The great quest for adopting AI-based computation for safety-/mission-critical applications
motivates the interest towards methods for assessing the robustness of the application wrt …

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

Modern Graphics Processing Units (GPUs) demand life expectancy extended to many years,
exposing the hardware to aging (ie, permanent faults arising after the end-of-manufacturing …

As convolutional neural network CNN accelerators are being adopted in emerging safety-
critical areas, their reliability becomes prominent. The systolic array is widely used as the …