Author: Emerald Knox (Emerald Knox)

Satwik Patnaik, Johann Knechtel, Mohammed Ashraf and Ozgur Sinanoglu Here we advance the protection of split manufacturing (SM)-based layouts through the judicious and well-controlled handling of interconnects. Initially, we explore the cost-security trade-offs of SM, which are limiting its adoption. Aiming to resolve this issue, we propose effective and efficient strategies to lift nets to...

Aditi Roy, Nasir Memon, Julian Togelius and Arun Ross Recent research has demonstrated the possibility of generating “Masterprints” that can be used by an adversary to launch a dictionary attack against a fingerprint recognition system. Masterprints are fingerprint images that fortuitously match with a large number of other fingerprints thereby compromising the security of a...

Jeff (Jun) Zhang, Tianyu Gu, Kanad Basu and Siddharth Garg Due to their growing popularity and computational cost, deep neural networks (DNNs) are being targeted for hardware acceleration. A popular architecture for DNN acceleration, adopted by the Google Tensor Processing Unit (TPU), utilizes a systolic array based matrix multiplication unit at its core. This paper deals...

Yanyan Zhuang,Albert Rafetseder, Yu Hu, Yuan Tian and Justin Cappos Due to their omnipresence, mobile devices such as smartphones could be tremendously valuable to researchers. However, since research projects can extract data about device owners that could be personal or sensitive, there are substantial privacy concerns. Currently, the only regulation to protect user privacy for...

Yiming Zhang , Quan Ho Vuong , Kenny Song , Xiao-Yue Gong and Keith W. Ross This paper considers entropy bonus, which is used to encourage exploration in policy gradient. In the case of high-dimensional action spaces, calculating the entropy and its gradient requires enumerating all the actions in the action space and running forward...

Jeff Zhang, Kartheek Rangineni, Zahra Ghodsi,  and Siddharth Garg Hardware accelerators are being increasingly deployed to boost the performance and energy efficiency of deep neural network (DNN) inference. In this paper we propose Thundervolt, a new framework that enables aggressive voltage underscaling of high-performance DNN accelerators without compromising classification accuracy even in the presence of high timing error...

Rui Zhang and Quanyan Zhu Distributed Support Vector Machines (DSVM) have been developed to solve large-scale classification problems in networked systems with a large number of sensors and control units. However, the systems become more vulnerable as detection and defense are increasingly difficult and expensive. This work aims to develop secure and resilient DSVM algorithms...