Researchers from Cornell have discussed the use of Transformer topologies, which are becoming increasingly popular due to their impressive performance in various domains such as computer vision, graphs, and multi-modal settings. Additionally, these models are capable of transfer learning, allowing them to quickly generalize to certain activities with no additional training. To address the energy consumption, speed, and feasibility of these models, researchers have proposed the use of hardware accelerators, such as GPUs, mobile accelerator chips, FPGAs, and large-scale AI-dedicated accelerator systems. Additionally, optical neural networks and analog computing have been suggested as solutions that provide better efficiency and latency than digital computers.
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