Our scientific and technological objectives can be summarized as follows:
- To study the theory, and develop algorithmic and architectural innovations for realizing adaptive and robust multi-timescale neural processing on mixed-signal analog/digital neuromorphic processors comprising both volatile and non-volatile memory devices to implement the synaptic circuits and TFT-based neurons.
- To develop novel hardware technologies that support on-chip learning with multiple time constants, both for synapses (volatile memory option combined with non-volatile memory, Electrochemical metallization, vacancy-type oxide-based memories, and Phase Change Memory), and neurons (TFT option exploration, plus integration with other devices).
- To study and develop an ultra-low-power, scalable and highly configurable neuromorphic computing processor capable of online, life-long learning for personalized neural learning and adaptation algorithms.
- To validate and demonstrate the project developments on realistic fully personalized edge application cases (by both simulation and board prototyping).