Speaker
Description
Neuromorphic computing systems mimic the structural architecture and computational strategies of the human brain, with the aim to boost the performance of artificial intelligence applications as well as simulations of biological brain dynamics. To reach this goal, several technologies exist, following different approaches.
Loihi 2, developed by Intel, leverages hardware implementation of spiking neural networks to achieve highly efficient and parallel processing capabilities and represents one of the most modern neuromorphic chips. SpiNNaker 2, a neuromorphic system very recently released by the University of Manchester and TU Dresden, focuses on offering high model flexibility while at the same time enabling highly efficient event-based communication. Both Loihi 2 and SpiNNaker 2 promise to offer tremendous scalability. On the other hand, there are memristive devices, which promise the creation of dense, energy-efficient circuitry due to their ability to emulate synaptic functionalities and retain memory without power supply. Furthermore, a novel approach is given by polymeric dendritic devices, which utilize organic materials to mimic the complex, tree-like structures of biological neurons, promising to offer flexible and potentially more bio-realistic computing systems. Last but not least, BrainScaleS, a mixed analog-digital neuromorphic platform, enables to tremendously accelerate brain-inspired computation by leveraging the real-time dynamics of electronic circuits.
Here, we present important systems and methodologies used in the field of neuromorphic computing and highlight their advantages and drawbacks. Our work entails comparative analyses between the aforementioned systems (Loihi 2, SpiNNaker 2, memristive devices, polymeric dendritic devices, and BrainScaleS), which shall elucidate the strengths and challenges of each system. By this, we aim to identify how these systems can contribute to advancing our understanding of cognitive processes as well as to technological applications.
