This paper presents DeepSecure, the an scalable and provably secure Deep Learning (DL)
framework that is built upon automated design, efficient logic synthesis, and optimization …

We propose CleaNN, the first end-to-end framework that enables online mitigation of
Trojans for embedded Deep Neural Network (DNN) applications. A Trojan attack works by …

Physical viability, in particular energy efficiency, is a key challenge in realizing the true
potential of Deep Neural Networks (DNNs). In this paper, we aim to incorporate the energy …

This paper proposes SSketch, a novel automated computing framework for FPGA-based
online analysis of big data with dense (non-sparse) correlation matrices. SSketch targets …

This paper proposes RISE, an automated Reconfigurable framework for real-time
background subtraction applied to Intelligent video SurveillancE. RISE is devised with a new …

We propose Perform-ML, the first Machine Learning (ML) framework for analysis of massive
and dense data which customizes the algorithm to the underlying platform for the purpose of …

This paper proposes CuRTAIL, a novel end-to-end computing framework for characterizing
and thwarting adversarial space in the context of Deep Learning (DL). The framework …

We propose SSketch, a novel automated framework for efficient analysis of dynamic big data
with dense (non-sparse) correlation matrices on reconfigurable platforms. SSketch targets …

A system may include a processor and a memory. The memory may include program code
that provides operations when executed by the processor. The operations may include …

Many neurodegenerative disorders affect cognitive and memory functions. This paper
presents work in progress of a tool which helps in the treatment of such disorders. To …