A substantial part of high energy consumption (> 60%) and large latency (> 90%) of conventional von-Neumann architectures can be attributed to the unavoidable data movement between the processor and main memory (DRAM). This is perhaps the major limiting factor for big data and machine learning applications whose usage is permeating into practically...

Synchronous logic remains the dominant design paradigm of digital systems such as Application Specific Integrated Circuits (ASICs). The conventional design of sequential circuit networks is based on the assumption that every register receives the clock signal at the same time. However, guaranteeing the simultaneity of clock arrival times in practice...

Efficient hardware/software codesigns of deep learning accelerators are crucial for optimizing their performance in various applications, ranging from datacenters to mobile and wearable devices. The existing methods for optimizing deep learning accelerator designs are primarily black-box approaches, which do not consider crucial information about the...

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...

­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...

Background Neuro-inspired machine deep learning have demonstrated that they can perform complex tasks such as image and speech classification, object recognition, and real-time decision-making. The state-of-the-art deep learning algorithms employ neural networks which have millions of parameters. Deploying such deep neural networks onto mobile platforms...

Artificial neural networks have countless applications with their ability for adaptive learning, recognition, and classification. When implemented in software, the artificial neural network algorithms often use a double-precision format. This can result in large area and power requirements for the hardware. Other approaches can be used to determine...