Memristor based synapse design and a case study in reconfigurable systems
Scientists have dreamed of an information system with cognitive human like skills for years. However, constrained by the device characteristics and rapidly increasing design complexity under the traditional processing technology, little progress has been made in hardware implementation. The recently popularized memristor offers a potential breakthrough for neuromorphic computing because of its unique properties including nonvolatily, extremely high fabrication density, and sensitivity to historic voltage/current behavior. In this work, we first investigate the memristor based synapse design and the corresponding training scheme. Then, a case study of an 8 bit arithmetic logic unit (ALU) design is used to demonstrate the hardware implementation of reconfigurable system built based on memristor synapses.
The 2013 International Joint Conference on Neural Networks (IJCNN)