SKM 2021 – scientific programme

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SYNC: Symposium Advanced neuromorphic computing hardware: Towards efficient machine learning

SYNC 1: Symposium: Advanced neuromorphic computing hardware: Towards efficient machine learning

SYNC 1.4: Invited Talk

Wednesday, September 29, 2021, 11:45–12:15, Audimax 1

Material learning with disordered dopant networks — •Wilfred van der Wiel — BRAINS Center for Brain-Inspired Nano Systems, MESA+ Institute for Nanotechnology, University of Twente, Enschede, The Netherlands

The implementation of machine learning in digital computers is intrinsically wasteful and one has started looking at natural information processing systems, in particular the brain, that operate much more efficiently. Whereas the brain utilizes wet, soft tissue for information processing, one could in principle exploit any material and its physical properties to solve a problem. Here we give examples of how nanomaterial networks can be trained using the principle of Material Learning to take full advantage of the computational power of matter.

We have shown that a designless network of gold nanoparticles can be configured into Boolean logic gates using artificial evolution. We further demonstrated that this principle is generic and can be transferred to other material systems. By exploiting the nonlinearity of a nanoscale network of boron dopants in silicon, we can significantly facilitate classification. Using a convolutional neural network approach, it becomes possible to use our device for handwritten digit recognition. An alternative Material Learning is approach is followed by first mapping our Si:B network on a deep neural network model, which allows for applying standard Machine Learning techniques in finding functionality. Finally, we show that the widely applied machine learning technique of gradient descent can be directly applied in materio, opening up the pathway for autonomously learning hardware systems.