Training of Physical Neural Networks
Physical Neural Networks (PNNs) leverage physical systems for computation, offering potential in AI. Research explores training larger models for local inference on edge devices. Various training methods are investigated, aiming to revolutionize AI systems by considering hardware physics constraints.
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The paper discusses Physical Neural Networks (PNNs), which utilize the properties of physical systems for computation, presenting a potential opportunity in modern AI. The authors explore the possibility of training AI models significantly larger than current ones and enabling local and private inference on edge devices like smartphones. Various training methods for PNNs are being investigated, including backpropagation-based and backpropagation-free approaches. While no method has yet matched the scale and performance of the widely used backpropagation algorithm in deep learning, the field is evolving rapidly. The research suggests that PNNs could revolutionize AI systems by creating more efficient current-scale models and enabling unprecedented-scale models. The study emphasizes the importance of considering hardware physics constraints in rethinking AI model functioning and training methods. This work highlights the potential for PNNs to reshape the landscape of AI systems with further research and development.
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9 commentsThe very thing that makes it so powerful and efficient is also the thing that make it uncopiable, because sensitivity to tiny physical differences in the devices inevitably gets encoded into the model during training.
It seems intuitive this is an unavoidable, fundamental problem. Maybe that scares away big tech, but I quite like the idea of having invaluable, non-transferable, irreplaceable little devices. Not so easily deprecated by technological advances, flying in the face of consumerism, getting better with age, making people want to hold onto things.
I am trying to understand what format does a node take in PNNs. Is it a transistor? Or is it more complex than that? Or, is it a combination of a few things such as analog signal and some other sensors which work together to form a single node that looks like the one we are all familiar with?
Can anyone please help me understand what exactly is "physical" about PNNs?
Last year, researchers from the University of Sydney and UCLA used NWNs to demonstrate online learning of handwritten digits with an accuracy of 93%.
This is a pretty big problem, though if you use information-bottleneck training you can train each layer simultaneously.
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