Our brain is a highly-structured but very complex network of a vast number of biological neurons. The brain is established on a completely different information processing principle from that of current digital computers, realizing its high cognitive performance through a physicochemical system. As a result, the brain can quickly and efficiently solve real-world problems, which the digital computers are bad at or cannot solve. Inspired by such information processing paradigm of the brain, in particular, focusing on information processing through physical dynamical process, we aim at a novel brainmorphic computing hardware system, which is robust and flexible, and yet quick and efficient.
Soft Computing Integrated System (Prof. Horio)
- Brainmorphic computing hardware
- Brain-inspired neuromorphic analog VLSI circuits
- High-performance brain-like information processing system and its applications
- Brain-inspired VLSI system with consciousness
We are working on a novel high-performance, highly-efficient, flexible, and robust brainmorphic computing hardware system. In particular, we focus on an information processing through physical complex-networked dynamical process, and its implementation as a computational hardware system using an analog VLSI as a core component. Toward to the final goal, we are developing integrated circuit and device technologies suitable for the brain-inspired computer systems, such as VLSI technologies for high-dimensional chaotic networks and large-scale complex systems, VLSI circuits and architectures for ultra-low-power asynchronous neural network systems, and compact and low-power devices/circuits, e.g., spintronics devices for neuron and adaptive synaptic connections. At the same time, we are developing a massively-parallel brain-inspired computational system architecture, which is very much different from that of the conventional digital computers. We further intend to realize an autonomous brain-inspired computer with a sense of self and consciousness based on a complex network with dynamic change in spatiotemporal network state and structure.