Training of Physical Neural Networks

by Anon84 • 2 years ago

142 46 2 years ago

So it sounds like these PNNs are essentially analog implementations of neural nets? Seems like an odd choice of naming to call them 'physical'.

tomxor8• 2 years ago

ANN is taken.

TheLoafOfBread• 2 years ago

I mean LoRA was taken too before LoRA became a thing

agarwaen163• 2 years ago

Well, this is particularly frustrating because PNN is already taken as well, by the (imo) much more novel idea of Physics Informed Neural Networks (aka PINNs or PNNs).

Why this isn't called Hardware Neural Nets is beyond me.

pessimizer• 2 years ago

Makes sense as opposed to "abstract." With the constant encoding and decoding that has to be done when things are going in an out of processors and storage (or sensors), digital processes are always in some sense simulations.

ksd482• 2 years ago

PNNs resemble neural networks, however at least part of the system is analog rather than digital, meaning that part or all the input/output data is encoded continuously in a physical parameter, and the weights can also be physical, with the ultimate goal of surpassing digital hardware in performance or efficiency.

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?

sigmoid10• 2 years ago

It's just a general idea to implement the computation part of neurons directly in hardware instead of software. For example by calculating sums or products using voltages in circuits, i.e. analog computing. The actual implementation is up to the designer, who in turn will try to mimic a certain architecture.

eightysixfour• 2 years ago

Here you go: https://arstechnica.com/science/2018/07/neural-network-imple...

Shawnecy• 2 years ago

My knowledge in this area is incredibly limited, but I figured the paper would mention NanoWire Networks (NWNs) as an emerging physical neural network[0].

Last year, researchers from the University of Sydney and UCLA used NWNs to demonstrate online learning of handwritten digits with an accuracy of 93%.

[0] = https://www.nature.com/articles/s41467-023-42470-5

programjames3• 2 years ago

That doesn't implement a trainable network on hardware, it's just creating a "reservoir" of associations between the inputs.

orbifold• 2 years ago

Classifying MNIST digits with 93% accuracy can also be accomplished using a linear classifier. So it isn't clear to me what the advantage would be.

tomxor8• 2 years ago

Last time I read about this the main practical difficulty was model transferability.

The 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.

bongodongobob• 2 years ago

Reminds me of the evolutionary FPGA experiment that was dependent on magnetic flux or something. The same program wouldn't work on a different FPGA.

cyberax• 2 years ago

Here's the paper about it: https://www.researchgate.net/publication/2737441_An_Evolved_...

And a more approachable article: https://www.damninteresting.com/on-the-origin-of-circuits/

rusticpenn• 2 years ago

What thy did was overfitting. We later found other ways of getting around the issue.

actionfromafar• 2 years ago

Would be interesting to hook up many FPGAs of the same model and train all of the at once. Programs with differing outputs on different individuals could be discarded. The program may still not transfer to another batch of FPGAs but at least you have a better chance of the working.

Another idea is to just train a whole bunch of them individually, like putting your chips in school. :-D

trextrex• 2 years ago

Well, the brain is a physical neural network, and evolution seems to have figured out how to generate a (somewhat) copiable model. I bet we could learn a trick or two from biology here.

tomxor8• 2 years ago

Some parts are copiable, but not the more abstract things like the human intellect, for lack of a better word.

We are not even born with what you might consider basic mental faculties, for example it might seem absurd, but we have to learn to see... We are born with the "hardware" for it, a visual cortex, an eye, all defined by our genes, but it's actually trained from birth, there is even a feedback loop that causes the retina to physically develop properly.

hansworst• 2 years ago

The way the brain does it is by giving users a largely untrained model that they themselves have to train over the next 20 years for it to be of any use.

chbint• 2 years ago

There's indeed a nice trick to be learned from cognitive science focused in biological cognition: the mind is embodied and embedded. Which means, roughly, that it is not portable. It doesn't store things like "glass at position x,y" but only "glass is at a small movement of the hand towards the right". Consequently, whatever gets encoded only makes sense within a given body and only inasmuch as it relied on its environment (with humans, that includes social environments). The good news is that, despite being not portable, this reliance on physical properties might be a step in the right direction, after all.

alexpotato2• 2 years ago

Last time I read about this the main practical difficulty was model transferability.

There is a great write up of this in this old blog post: https://www.damninteresting.com/on-the-origin-of-circuits/

robertsdionne• 2 years ago

This is “Mortal Computation” coined in Hinton’s The Forward-Forward Algorithm: Some Preliminary Investigations https://arxiv.org/abs/2212.13345.

programjames3• 2 years ago

You can regularize the networks to make them transfer easier. I can't remember the abstract's title off the top of my head though.

dsabanin• 2 years ago

Couldn’t you still copy by training a new network on a new device to have same outputs for the same inputs as the original?

tomxor8• 2 years ago

Yes, but training is the most expensive part of ML, for example GPT-3 is estimated to cost something like 1-4 million USD.

With ANN you can do it one time and then clone the result for negligible energy cost.

Maybe training a batch of PNNs in parallel could save some of the energy cost, but I don't know how feasible that is considering they could behave slightly differently during training causing divergence... Now that sarcastic comment at the bottom of this thread is starting to sound relevant "Schools".

etiam• 2 years ago

Distillation (as you may be aware). https://arxiv.org/abs/1503.02531

Having to do that in each instance is still really cumbersome for cheap mass deployment compared to just making a digital-style exact copy, but then again I guess a main argument for wanting these systems is that they'd be doing things unachievable in practice on digital computers.

In some cases one might be able to distill to digital arithmetic after the heavy parts of the optimization are done, for replication, distribution, better access for software analysis, etc.

CuriouslyC• 2 years ago

This was the thing Geoff Hinton cited as a problem with analog networks.

I think eventually we'll get to the point where we do a stage of pretraining on noisy digital hardware to create a transferrable network, then fine tune it on the analog system.

jegp• 2 years ago

It's still possible to train a network that's aware of the physics and then transfer that to physical devices. One approach to this from the neuromorphic community (that's been working on this for a long time) is called the Neuromorphic Intermediate Representation (NIR) and already lets you transfer models to several hardware platforms [1]. This is pretty cool because we can use the same model across systems, similar to a digital instruction set. Ofc, this doesn't fix the problem of sensitivity. But biology fixed that with plasticity, so we can probably learn to circumvent that.

[1] https://github.com/neuromorphs/nir (disclaimer: I'm one of the authors)

6gvONxR4sf7o• 2 years ago

If (somehow/waves hands) you could parallelize training, maybe this would turn into an implicit regularization and be a benefit, not a flaw. Then again, physical parallelizability might be an infeasibly restrictive constraint?

craigmart• 2 years ago

Schools?

programjames3• 2 years ago

These methods are typically slow because the number of gradient updates scales linearly with the number of learnable parameters in the network, posing a significant challenge for scaling up.

This is a pretty big problem, though if you use information-bottleneck training you can train each layer simultaneously.

Training of Physical Neural Networks

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