Thermodynamic Computing Advances with Design and Training

According to Whitelam and Casert, when the components of the computer are themselves nonlinear, it becomes possible to train a thermodynamic computer to perform nonlinear computations at specified times, regardless of its equilibrium status. This means the computer operates more like a classical computer, without the need to wait for equilibrium. It also expands the set of thermodynamic algorithms to the same types of complex, nonlinear problems a neural network can do, meaning thermodynamic computing could be an appropriate tool for machine-learning workloads that have previously been outside its capabilities.

“A nonlinear thermodynamic circuit can behave like a neuron in a neural network,” said Whitelam. “Nonlinearity is what gives a neural network its expressive power. What we reasoned is that if you build these thermodynamic neurons into a connected structure, then that structure should have the expressive power to mimic a neural network and so be able to do machine learning.” 

Together, these solutions expand what thermodynamic computing can do.

Inverted training

The challenge, then, becomes training such a system. A thermodynamic computer is a stochastic system, meaning that no two runs on a thermodynamic computer look the same, and the methods used for training digital neural networks don’t apply. But Whitelam and Casert have offered a solution there as well.

To train Whitelam’s model of the thermodynamic computer, Casert engineered a large-scale computational framework. Using 96 GPUs in parallel on the Perlmutter supercomputer at NERSC, Casert built and ran massively parallel evolutionary simulations, evaluating billions of noisy dynamical trajectories per generation to discover the most effective network parameters. 

In particular, he used a framework known as a genetic algorithm: beginning with a set of different thermodynamic neural networks and evaluating the effectiveness of each, he selected the best-performing and mutated them, adding random noise to their parameters, and evaluated them again. Ultimately, Casert simulated more than a trillion runs of a thermodynamic computer, using Perlmutter’s GPUs in parallel. This training framework is considerably more costly than the methods used to train digital networks, but it yields a computer that can operate using very little energy after it’s built and trained.

“It’s a very different way of optimizing a neural network. Training a thermodynamic neural network by simulating it digitally is expensive, but once trained and built as physical hardware,  we can perform inference on that hardware for a very low energy cost,” said Casert. 

The combination of design and training show that a machine-learning computer that uses far less energy is possible. 

More hardware, more algorithms

The field of thermodynamic computing is relatively young – so where does it go from here? According to Whitelam, it’s important to work out how to realize these designs in hardware. Currently, the team is looking for experimental partners to make both hardware and software a reality — another step exploring what’s possible with thermodynamic computing.

Another step, he says, is more algorithms. Existing algorithms are meant for systems working at equilibrium; with that requirement no longer a roadblock, new ones will need to be developed. The field will also need new algorithms for nonlinear computations, mirroring the ones used for digital neural networks. 

“It’s an exciting field,” said Whitelam. “We’re looking for more efficient ways of computing, and thermodynamic computing is definitely one of them.”

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