Memristive self-organizing dendrite networks for brain-inspired computing

Artificial Intelligence needs a hardware revolution to sustain the ever-growing demand of computing power in our society, where the huge energy consumption and environmental impact of computation with current technologies is unsustainable. In the race toward future computing, bioinspired technologies have been shown as promising hardware solutions for computing beyond the Turing model and the classical von Neumann architectures. Going beyond transistor-centred hardware solutions, the research community is exploring new device concepts and architectures that leverage physical phenomena for computing “in materia” with physical laws to emulate the effectiveness of information processing capabilities of our brain. While arrays of memristive devices realised with a top-down approach represent emerging solutions for the hardware realisation of artificial neural networks, these systems do not emulate the topology and emergent behaviour of biological neuronal circuits where the principle of self-assembly and self-organisation regulates both structure and functions, providing adaptability, efficiency, and robustness. Tackling main challenges of neuromorphic computing, the MEMBRAIN project aim to develop a radically new concept of physically grounded computing nanoarchitecture based on self-organising memristive nanonetworks of dendrites, able to efficiently process information and to store knowledge on the same physical substrate at the matter level through physical laws. Overcoming the concept of nanotechnology as a simple advancement of microtechnology, the ambition is to compute like nature – thermodynamically – to push computation near fundamental limits of efficiency. By establishing a hardware-software codesign framework at the crossroads of material science, machine learning and neuroscience, the aim is to retarget the original goal of neuromorphic computing of creating general-purpose truly intelligent systems that endow dynamic learning and multitasking capability.