Neuromorphic computing and AI
Microelectronics and computers have revolutionized our way of life. Massive integration of semiconductor devices, fueled by an incredible stream of materials innovation, has provided us with tools to connect, sense, analyze, control, produce and make decisions in completely new ways.
Artificial intelligence (AI) is the ability to perform tasks that are generally associated with intelligent beings. Recently, bio- and neuro-inspired (neuromorphic) algorithms have attracted considerable attention with their ability to extract structure and knowledge from huge unstructured data sets by relying solely on limited domain expert knowledge. It will have an even greater impact on our way of life than the invention of the Internet.
To execute these new algorithms efficiently at the large scale required in datacenters or, for example, to interpret sensor data locally in embedded, very low-power solar-powered devices, we need novel neuromorphic compute architectures and hardware.
The Neuromorphic Devices & Systems group has a strong and long-standing track record in compute-system architectures and semiconductor materials and devices, which we are applying today to solve the computational challenges of neuromorphic and AI computing.
The goal of our research is to develop new materials and devices for electronic and photonic neuromorphic computing systems, to invent processes and technologies to fabricate such devices and circuits, and to demonstrate power-efficient neuromorphic computing hardware.
Designing optimal strategies and architectures for accelerating existing and future neuromorphic workloads.
Analog computing devices
Exploring how to offload computationally expensive operations from digital processors to specialized accelerators.
Developing materials for devices with variable interconnection strength or plasticity.