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This Tutorial describes the building blocks of neuromorphic VLSI systems and the way they are engineered. From learning mechanisms through the gap between reactive and cognitive systems, all major aspects are covered.
Biological systems have many key answers for our current limitations in scaling. Miniaturization and more speed were the driving forces for VLSI technology in past few decades. As we are reaching the dead end of Moore's law, now the paradigm has shifted towards intelligent machines. Many efforts are made to mimic the commonsense observed in animals. Automation and smart devices have taken a great...
Recent advances in neuromorphic engineering for brain-like computing and neural prostheses are converging towards realization of electronic synaptic arrays approaching the integration density and energy efficiency of the human brain. A major impediment in this development is practical realization of complex conductance-based models of biophysical neural and synaptic dynamics in nanoscale electronics...
Neural associative networks are a promising computational paradigm, both for modeling neural circuits of the brain and implementing Hebbian cell assemblies in parallel VLSI or nanoscale hardware. Previous works have extensively investigated synaptic learning in linear models of the Hopfield-type and simple non-linear models of the Steinbuch/Willshaw-type. For example, optimized Hopfield networks of...
Artificial neural networks are nonlinear dynamic systems which are constituted of abundant simple processing unites, their characteristics include parallel computing, memory distributing, self-learning and self-organization, etc. They are used to solve many artificial intelligence problems that are very difficult or impossible to be solved by traditional computer. Based on theory of the semiconductor...
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