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...

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...

­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...

­Background In the era of artificial intelligence, various deep neural networks (DNNs), such as multi-layer perceptron, convolutional neural networks, and recurrent neural networks, have emerged and achieved human-level performance in many recognition tasks. These DNNs usually require billions of multiply-and-accumulate (MAC) operations, soliciting...