How Spiking Neural Networks Can Solve AI’s Carbon Footprint Problem (and Other Challenges)

We’re well into the AI revolution, with the global AI market in 2023 estimated at more than $200 billion. McKinsey predicted last year that AI could add more than $25 trillion to the global economy. 

Despite their popularity and utility, however, so-called traditional machine learning (ML) and neural networks aren’t perfect. For one thing, many traditional AI systems don’t align well with biological neural functions. And they also use a lot of energy when making computations.

Spiking neural networks (SNNs), however, could change all of that. Here’s how.

What are Spiking Neural Networks (SNNs)?

Spiking neural networks are a type of artificial neural network that closely mimic biological neural networks – in other words, our brains. Alan Hodgkin and Andrew Huxley designed the first SNN in 1952.

Inspired by the firing of biological neurons in the brain and how they communicate, SNNs operate via discrete, temporal spikes of activity, unlike traditional neural networks, which process continuous data. That means only a small number of neurons in an SNN are ever active at the same time.

SNNs are sometimes also referred to as the “third generation” of neural networks, with their main differentiator being their ability to resemble biological neurons more closely. Neurons in our brains also communicate through similar brief, event-driven spikes in activity.

Today, traditional neural networks are primarily powered by artificial neural networks (ANNs), which are the bedrock of modern deep learning systems and algorithms.  

How Do SNNs Work?

Because SNNs incorporate amplitude (signal strength) and time into their model, they’re uniquely positioned to handle tasks requiring real-time processing. Indeed, because SNNs are inherently event-driven, they process information only when a spike occurs – the same way our brains conserve energy. 

In an SNN, neurons fire or spike when the input they receive exceeds a certain threshold, and these spikes propagate through the network. The precise timing of when these spikes occur relative to each other is also a source of information for an SNN model. SNNs use optimizers and special loss functions, such as spike-timing-dependent plasticity, which makes these models structurally well-suited to real-time tasks. 

Unlike other AI systems, however, SNNs perform best when paired with a particular type of computing hardware. Neuromorphic chips also attempt to mimic the way the brain processes information. 

That means neuromorphic hardware is ideally suited for SNNs in applications that require real-time processing and energy efficiency, such as autonomous vehicles and robotics.  

Many observers have noted that SNNs combined with neuromorphic hardware have the potential to revolutionize AI and ML because of this computational efficiency, which emulates efficiencies developed over thousands of years of human evolution. Working SNN models include the Leaky Integrate-and-Fire (LIF) and Izhikevich models. 

What is the Value of SNNs?

The close alignment of SNNs with biological processes can help address the challenges faced by traditional AI through real-time processing capabilities and superior efficiency. The advantages of SNNs include:

• Energy efficiency: You’d drain a lot of energy if all the neurons in your brain fired constantly, and the same principle is true for artificial neural networks. The event-driven processing nature of SNNs makes them much more energy efficient than traditional deep learning and AI systems, which can be carbon intensive. 

• AI’s carbon footprint is around one percent of global emissions and has increased 300,000-fold since the early 2010s. Training GPT-3 produced more than 500 metric tons of carbon dioxide equivalent. 

• SNNs, on the other hand, have been estimated to save energy by orders of magnitude over traditional AI.

• Biological realism: By closely mimicking the behavior of biological neurons, SNNs hold great promise in uncovering the secrets of natural brain function. Their inherent closeness to natural processes means they’ve also increasingly been used to facilitate interdisciplinary research between neuroscience and artificial intelligence and can learn from fewer examples.

• Time-dependent processing: SNNs are particularly suited for tasks where the timing of events is crucial, such as processing sensory data or sequencing. While traditional ANNs usually require additional tools—such as long short-term memory units (LSTMs)—to deal with temporal dynamics, the concept of time is baked into the computations of SNNs. 

SNNs, however, also have challenges. Training them is often a difficult and complex proposition, for example. They currently lack efficient and scalable learning algorithms. The relative scarcity of neuromorphic hardware means SNNs currently have few opportunities to demonstrate their potential. 

What Are the Applications of SNNs?

SNNs have shown promise across a range of practical applications; in theory, they can be used for any application suitable for standard ANNs. Their real-time processing capabilities make them especially useful for applications requiring extremely fast decision-making, such as robotics, speech recognition, natural language processing, computer vision, medical diagnostics, and autonomous vehicles.

SNNs are also suited for more advanced applications such as brain simulation and robotic embodied cognition. They’ve even shown to be adept at stimulating natural cells. 

Conclusion

Although SNNs have existed for decades, they’ve begun to attract intense attention in the research community due to their energy efficiency and ability to mimic natural brain functions. 

Many AI experts consider the growing momentum of SNNs the next great leap forward in AI technology. SNNs combined with neuromorphic hardware offer the potential for a new wave of more powerful, efficient, and intuitive AI applications.

CapeStart’s ML engineers and data scientists work with clients to develop and implement advanced AI, ML, and natural language processing (NLP) models to drive efficiency and business outcomes. Contact us today to learn how AI can help scale your innovation.