Systematic configuration and automatic tuning of neuromorphic systems

Sadique Sheik, Fabio Stefanini, Emre Neftci, Elisabetta Chicca, Giacomo Indiveri
2011 2011 IEEE International Symposium of Circuits and Systems (ISCAS)  
In the past recent years several research groups have proposed neuromorphic Very Large Scale Integration (VLSI) devices that implement event-based sensors or biophysically realistic networks of spiking neurons. It has been argued that these devices can be used to build event-based systems, for solving real-world applications in real-time, with efficiencies and robustness that cannot be achieved with conventional computing technologies. In order to implement complex event-based neuromorphic
more » ... ms it is necessary to interface the neuromorphic VLSI sensors and devices among each other, to robotic platforms, and to workstations (e.g. for data-logging and analysis). This apparently simple goal requires painstaking work that spans multiple levels of complexity and disciplines: from the custom layout of microelectronic circuits and asynchronous printed circuit boards, to the development of object oriented classes and methods in software; from electrical engineering and physics for analog/digital circuit design to neuroscience and computer science for neural computation and spike-based learning methods. Within this context, we present a framework we developed to simplify the configuration of multi-chip neuromorphic VLSI systems, and automate the mapping of neural network model parameters to neuromorphic circuit bias values. 978-1-4244-9474-3/11/$26.00 ©2011 IEEE
doi:10.1109/iscas.2011.5937705 dblp:conf/iscas/SheikSNCI11 fatcat:umo2lfryzzdsxkbosjvvrcz4ra