The architecture information of a Deep Neural Network (DNN) model is considered a valuable, sensitive piece of property for a company. Knowledge of a DNN’s exact architecture allows any adversary to build a substitute model and use this substitute model to launch devastating adversarial attacks. Side-channel based DNN architecture stealing can...

Traditional von-Neumann computing architectures, such as CPUs and GPUs, demonstrate limitations in memory bandwidth and energy efficiency. However, their high demand lies in their programmability and flexible functionality. Such platforms execute a wide spectrum of bit-wise logic and arithmetic operations. In this regard, recent application-specific...

The success of conventional supervised learning relies on large-scale labeled datasets to achieve high accuracy. However, annotating millions of data samples is labor-intensive and time-consuming. This promotes self-supervised learning as an attractive solution with artificial labels being used instead of human-annotated ones for training. Contrastive...

Deep neural networks (DNNs) have shown extraordinary performance in recent years for various applications, including image classification, object detection, speech recognition, etc. Accuracy-driven DNN architectures tend to increase model sizes and computations in a very fast pace, demanding a massive amount of hardware resources. Frequent communication...

Nowadays, one practical limitation of deep neural networks (DNNs) is their high degree of specialization to a single task. This motivates researchers to develop algorithms that can adapt the DNN model to multiple tasks sequentially, while still performing well on past tasks. This process of gradually adapting the DNN model to learn from different...

­One practical limitation of deep neural network (DNN) is its high degree of specialization to a single task or domain (e.g., one visual domain). This motivates the development of algorithms that can adapt DNN model to multiple domains sequentially while still performing well on past domains. This is known as multi-domain learning. Conventional...

­The study of human genetics is a rapidly expanding field, fueled in part by developments in large-scale protein and genomic sequencing technologies. Biopharmaceutical companies and modern healthcare rely heavily on sequencing technologies and the acquired data to develop new drugs and provide effective treatments to patients. However, the results...

­In the era of big data, min/max searching from bulk data arrays is one of the most important and widely used fundamental operations in data-intensive applications such as sorting, ranking, bioinformatics, data mining, graph processing, and route planning. Online news and social media require real-time ranking using fast min/max searching from...

­Recently, Deep Neural Networks (DNNs) have been deployed in many safety-critical applications. The security of DNN models can be compromised by adversarial input examples, where the adversary maliciously crafts and adds input noise to fool a DNN model. The perturbation of model parameters (e.g., weight) is another security concern, one that relates...

­Background Deep neural networks (DNNs) have been very successful in large-scale recognition tasks, but they exhibit large computation and memory requirements. To address the memory bottleneck of digital DNN hardware accelerators, in-memory computing (IMC) designs have been presented to perform analog DNN computations inside the memory. Recent IMC...