Neuroscience × Emerging Systems
Neuroscience and Brain Network
Recently, the human connectome (the structural and functional connectivity patterns of the human brain) has aroused great interests of neuroscientists and attracted large amounts of investment and public attention (e.g. the Brain Initiative Project). Currently, the analysis of non-invasive neuroimaging data has faced two critical challenges. Firstly, an increasing number and size of datasets are generated in the community, which leads to very high requirements for the computational capabilities. Second, the high-resolution neuroimaging data requires more efficient and reasonable statistical methods for the high dimensional data analysis.
The amount of data in our world is exploding at an astounding rate. We have entered the 'Era of Big Data'. Large scale neural networks, also known as the deep neural networks (DNNs) or deep learning, have demonstrated a great promise in processing Big Data. State-of-the-art performance has been reported in many unstructured data processing tasks, ranging from visual object classification, speech recognition, to nature language processing and information retrieval. In our work, we propose a series of Energy Efficient System Design for Neural Networks.