In-Materio Computing Devices Composed of Random Network Nanoparticles for Future Autonomous Robot Operation

In recent years, the superior computational power of deep learning based on software has been widely recognized, and the practical applications of artificial intelligence are rapidly expanding. On the other hand, the hardware for replacing to such artificial intelligence (AI) algorithms is facing the physical limits of scaling in silicon CMOS technology, and performance improvement is expected to hit the ceiling. For this reason, there is a growing interest in hardware technologies that physically implement artificial neural networks (ANNs), neuromorphic information processing systems, and the applications, as well as new materials and devices. Here, we report results on materials that can complement CMOS for AI systems such as organic polymers and nanocarbon materials. We will also report further trial to apply the materials to autonomous AI robots.