Dealing with big data, especially the videos and images, is the biggest challenge of existing Von-Neumann machines while the human brain, benefiting from its massive parallel structure, is capable of processing the images and videos in a fraction of a second. The most promising solution, which has been recently researched widely, is brain-inspired computers. The brain-inspired computers, so-called neuromorphic computing systems (NCSs), overcome the limitation of the word-at-a-time thinking of conventional computers benefiting from massive parallelism for data processing, similar to the brain.

There are different implementations of NCSs using analog/digital CMOS technology and beyond CMOS technologies such as memristive devices, spin-based elements (magnetic tunnel junctions, spin-torque nano oscillators, etc.), phase change materials, etc. In this group, our focus is on further performance improvement of NCSs at the circuit and system levels. At the circuit level, novel neuron and synapse design as well as novel peripheral circuits for NCSs are exploring with the clear target of improving the speed, energy, and density. At the system level, we are working on performance improvement of NCSs by novel multidisciplinary structures.