TY  - BOOK
AB  - 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 systems 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.
DA  - 2011
DO  - 10.1109/ISCAS.2011.5937705
KW  - VLSI
KW  - neural nets
LA  - eng
PY  - 2011
SN  - 978-1-4244-9473-6
TI  - Systematic configuration and automatic tuning of neuromorphic systems
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-24278222
Y2  - 2024-11-22T09:19:36
ER  -