Chinese scientists develop brain-inspired AI model to cut energy use

The turnaround which the team’s findings can lead to is a system of AI development which does not rely on silicon-based processors or chips.

Need for less power-consuming AI systems

While it has many applications, the growth of AI has also led to an increased demand for energy. The AI models are large energy consumers and with an ever-expanding role of AI in all fields, it is only going to increase.

On the opposite end of the spectrum sits the human brain, more complex than any other AI model created till date, which requires just a tiny fraction of energy to fulfill all its duties.

Therefore, scientists from the Chinese Academy of Sciences’ Institute of Automation, and Peking University got together with the aim to develop an AI model that can do more tasks with much smaller energy consumption.

The AI model’s functioning is much simpler and it intends to come up with several general-purpose models which would likely have better cognitive abilities.

New model based on internal workings of human brain

The scientists associated with the project refer to the new model as an “internal complexity model.” It has been so named as it tries to replicate the inner workings of the brain to complete tasks by using a fraction of energy.

According to a report by Xinhua, the tests carried out by the research team proved the effectiveness of the internal complexity model in carrying out numerous tasks.

The experiments also showed that it could provide new methods and theoretical support for integrating the dynamic characteristics of neuroscience into AI, and also offering feasible solutions for optimizing and enhancing the practical performance of AI models.

According to the paper, the researchers were able to build a Hodgkin-Huxley (HH) network with rich internal complex to prove that its performance is equal to those generated by the much bigger leaky integrate-and-fire (LIF) network.

The study was published recently in the journal Nature Computational Science.

Abstract

Artificial intelligence (AI) researchers currently believe that the main approach to building more general model problems is the big AI model, where existing neural networks are becoming deeper, larger and wider. We term this the big model with external complexity approach. In this work we argue that there is another approach called small model with internal complexity, which can be used to find a suitable path of incorporating rich properties into neurons to construct larger and more efficient AI models. We uncover that one has to increase the scale of the network externally to stimulate the same dynamical properties. To illustrate this, we build a Hodgkin–Huxley (HH) network with rich internal complexity, where each neuron is an HH model, and prove that the dynamical properties and performance of the HH network can be equivalent to a bigger leaky integrate-and-fire (LIF) network, where each neuron is a LIF neuron with simple internal complexity.