This June 2024 paper examines the current state and future potential of Physical Neural Networks (PNNs), which are AI systems implemented directly in physical hardware rather than purely digital software. It explores various training methodologies for PNNs, including in-silico (digital simulation), in-situ (real-world hardware training), and hybrid approaches like physics-aware training, each with its own advantages and limitations regarding accuracy, speed, cost, and complexity. The text also discusses alternative training paradigms such as Feedback Alignment, Local Learning, and gradient-free methods that aim to overcome challenges associated with traditional backpropagation in physical systems. Furthermore, it highlights the promise of PNNs for advanced applications like continual learning and the development of energy-efficient large AI models, identifying emerging technologies such as quantum and photonic hardware as key to their future scalability and performance benefits over conventional digital systems.
Source:
https://arxiv.org/pdf/2406.03372
