Despite the numerous efforts of security researchers, memory vulnerabili...
I/O efficiency is crucial to productivity in scientific computing, but t...
In this work, we propose an open-source, first-of-its-kind, arithmetic
h...
Long training times of deep neural networks are a bottleneck in machine
...
Binary analysis is traditionally used in the realm of malware detection....
In Internet of Things (IoT) systems with security demands, there is ofte...
Deep Neural Network (DNN) workloads are quickly moving from datacenters ...
In this work, we introduce a platform for register-transfer level (RTL)
...
The development and implementation of post-quantum cryptosystems have be...
Due to the rapid advances in the development of quantum computers and th...
Many cybersecurity attacks rely on analyzing a binary executable to find...
Many computer organization and computer architecture classes have recent...
The widespread application of artificial neural networks has prompted
re...
In this work, we introduce a Self-Aware Polymorphic Architecture (SAPA)
...
Sphinx, a hardware-software co-design architecture for binary code and
r...
Fully-connected layers in deep neural networks (DNN) are often the throu...
Graph algorithms and techniques are increasingly being used in scientifi...
To avoid packet loss and deadlock scenarios that arise due to faults or ...