Nano Neuromorphic Computing

As AI processors increasingly demand more energy, advances in nanotechnology and material science offer a solution. Our team harnesses the potential of advanced nanodevices to achieve more sustainable circuits.

Spintronic materials, superconductors, or oxides possess rich physics that enables them to dynamically process information in intricate ways, as well as to store it locally across various time scales. This allows for the execution of complex tasks at the nano-scale within integrated circuits, and provides alternatives to the energy-intensive CMOS technology.

We are constructing experimental neural networks that exploit the unique properties of these materials, both at the device level, mimicking synapses and neurons, and at the system level by leveraging their capacity to dynamically interconnect and evolve collectively.

Our aim is to design specialized accelerators that outperform CMOS in efficiency and unlock capabilities previously unachievable with CMOS through the seamless integration of nanodevices in large-scale hybrid architectures.