A review on methods, issues and challenges in neuromorphic engineering

Abstract

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 momentum in recent years, but to imitate human intelligence in machines with existing hardware is not viable. By only developing complex algorithms and implementing software, intelligence is not accomplishable. This paper brings out the very basic differences between brain and computers. The complexity of human brain and flaws of current computing architecture is discussed. Neuromorphic Engineering emerges as a realistic solution whose architecture and circuit components resemble to their biological counterparts. This paper acts as a primer for Neuromorphic engineering. Brain functionality being analogous, the limitation of only digital systems is addressed by the mixed mode operation of ICs. Modelling of Neuron, various software and hardware available to realize these morphed architectures are studied. The gap between the software simulation and Hardware emulation, FPGA and VLSI implementation is debated. To reach to a larger audience the paper exposes many limitations of Neuromorphic engineering and come out with many open research issues.