SWS Technologies Artificial Intelligence is transforming our world. Chatbots, Virtual assistants, Autonomous vehicles, Diagnosis and Scientific applications all use AI. However, this comes at a large cost. Most modern AI models use Graphics Processing Units heavily. Whilst this has allowed massive advancement in AI, they require a lot of energy and also require complicated cooling systems.
Can we make artificial intelligence smarter without requiring it to have a large energy consumption?
Thankfully we can look to the human brain for inspiration. Using brain inspired computing, we can make our AI models intelligent whilst having a much reduced energy consumption. Technologies such as neuromorphic computing and spiking neural networks could help us move beyond the traditional GPU based AI systems.
The Energy Problem of Modern AI
Modern AI models require large mathematical calculations, big data sets and special hardware. Advanced models require thousands of processors to run for long periods of time. Once deployed, they will be run by millions of users requiring constant computation.
GPUs are extremely good at performing parallel computing making them great for Deep Learning. However, they require high energy to run traditional AI models that involve lots of mathematical operations.
There are a few reasons why modern AI uses so much energy:
Large amounts of computation due to the high quantity of mathematical operations required.
Lots of memory usage as data must travel between the processors and memory.
Cooling is required for data centres.
More generative AI and intelligent systems are being used.
Whilst hardware is constantly becoming more efficient, AI is getting bigger and there is a difficult balance between performance, cost and sustainability. This has led researchers to investigate alternative computing systems.
Inspiration from the Human Brain
The human brain is one of the most amazing information processing systems. We have approx 86 billion neurons with trillions of synapses.
Despite being an extremely complex piece of hardware the human brain (once an adult) only uses around 20 watts of power.
This is because:
Traditional computers run operations in a structured way. Modern AI accelerators have lots of mathematical operations and run in parallel. Whereas biological brains run in a distributed way using dynamic connections, asynchronous communication and lots of local computation.
Whilst this may not always be the case, the brain can be much more efficient for certain tasks. Computers can perform lots of calculations much faster and with greater accuracy. However we can learn a lot from the brain to produce AI that is much more selective with its energy consumption.
What is Brain Inspired AI?
This involves using computing systems that are inspired by biological neural systems. Rather than traditional artificial neural networks and hardware, alternative methods of information processing are explored.
There are three main areas of interest.
1. Neuromorphic Computing
This looks to develop hardware that mimics parts of the neural systems found in the brain. It involves computation, memory and communication that can reduce the amount of data being moved and therefore saving energy.
Traditional computer systems will often have separate memory and processors meaning data must be moved between them. However, neuromorphic systems can bring memory and computation together for more local computation.
Some neuromorphic chips use specialised circuits that mimic neurons and synapses. Examples of this include Intel’s Loihi research processors, and IBM’s NorthPole architecture. This looks to use brain inspired principles for efficient AI inference.
These can be used for robotics, sensors, pattern recognition and other areas. As they can operate quickly and use little power.
2. Spiking Neural Networks
Most traditional deep learning systems use artificial neurons and pass mathematical data between them. Whereas spiking neural networks use asynchronous communication.
This is inspired by biological neurons.
Rather than constantly calculating and passing data, spiking neural networks will only fire signals when certain conditions are met. If little data is being passed, then less computation will be required.
For example, traditional vision systems will scan all pixels in a video frame. However, using an event based vision system only events where changes in brightness are seen.
This can reduce the amount of information being processed.
However, they will only be efficient for certain tasks, networks, hardware and software. They can also be difficult to train and may not always offer improvements in comparison to traditional neural networks.
3. Event Based Computing
This is similar to spiking neural networks, however is a general principle. Only compute when something meaningful happens.
Take an intelligent security camera. A traditional system would constantly analyse video frames. However, an event based system will only focus on motion, anomalies or major changes.
This can reduce unnecessary computation.
This can be extremely useful for battery powered sensors, wearables, drones and autonomous robots. As less energy is used the battery life can be increased and intelligent processing can be done on the device.
Useful Applications of Energy Efficient Brain Inspired AI
There are several areas where brain inspired computing could be useful.
Healthcare & Wearables
Many wearables involve constantly collecting data such as movement, heart rate etc. Using energy efficient neural networks could help identify meaningful changes locally.
This could lead to longer battery life and quicker responses. However it would need to be validated thoroughly for use in clinical settings.
Autonomous Robotics
Robots need to understand their environment, avoid obstacles and make decisions. This can be difficult to do on low power devices.
Using neuromorphic processors and event based sensors can help robots process changing environments. This makes them a good choice for small autonomous robots, drones and industrial systems.
Smart Cities & IoT
Smart cities use lots of sensors to monitor traffic, air quality, infrastructure and energy. Local processing can help reduce data transmission.
Using brain inspired AI can give these systems more energy efficiency. As they can respond to meaningful events.
Edge AI
Remote environments may have sensors that need to run for months or years without reliable power. Using low power AI can help with identifying fires in forests, strange animal movements, changes in water quality etc.
Using brain inspired systems can help make intelligent decisions on the device. This can help reduce reliance on cloud computing.
GPUs vs. Brain-Inspired Computing
GPUs and brain-inspired systems should not necessarily be viewed as competing technologies. They are different tools designed around different computational strengths.
| Feature | GPU-Based AI | Brain-Inspired Computing |
|---|---|---|
| Processing approach | Highly parallel numerical operations | Often event-driven neural processing |
| Data representation | Usually continuous numerical values | May use discrete spikes or other neural representations |
| Memory and computation | Often separated, though modern GPUs use sophisticated local memory | May integrate or closely couple memory and processing |
| Energy efficiency | Varies with workload, model, and hardware | Potentially very efficient for suitable sparse workloads |
| Training ecosystem | Mature frameworks and extensive tooling | Developing tools and training methods |
| Typical strengths | Large-scale deep learning, generative AI, scientific computing | Low-power sensing, real-time responses, robotics, edge AI |
The central distinction is that GPUs are highly effective at executing large volumes of dense mathematical operations, while brain-inspired systems can be advantageous when information is sparse, asynchronous, or continuously changing.
The most practical future may combine these approaches rather than replace one with the other.
Issues: Will Brain Inspired AI replace GPUs?
Whilst brain inspired AI can be promising, it isn’t a catch-all solution.
The hardware isn’t as advanced. Neuromorphic hardware isn’t as readily available as traditional GPUs. It also lacks mature tools and software.
It can be difficult to train. Traditional neural networks have well established Gradient Descent training methods. However spiking neural networks often need specialist methods. This can make it difficult to get competitive performance on complex tasks.
It depends on what you’re doing. Whilst sparse and asynchronous tasks can benefit, dense matrix operation and large generative models may perform better on GPUs or other traditional accelerators.
Comparing can be difficult. Just knowing how much power a chip uses won’t tell you anything about the AI. You need to do honest comparisons. This should take into consideration the whole task, accuracy of the model, training/inference costs, memory, data movement, cooling and energy used.
It isn’t necessarily efficient. Just because it mimics neurons doesn’t mean it will be efficient like the brain.
Overall brain inspired computing should be seen as a promising solution for specific AI tasks.
Future: Hybrid Computing Systems
The next generation of AI infrastructure may use a variety of computing methods.
GPUs could be used to train and run large foundation models. Neuromorphic processors could be used for asynchronous perception, understanding sensors and low power. Other specialist accelerators could be used to optimise certain operations. CPUs can be used to manage the system as a whole.
This can help with intelligent computation.
An autonomous robot could use an asynchronous vision sensor and neuromorphic processor to identify nearby obstacles. However, complex planning or language interaction could be done using traditional accelerators. This means only the parts needed for each task are running at full capacity.
This helps show that energy efficiency relies on better algorithms, smaller models, better memory systems and making decisions on when/where to compute.
Ultimately we want to make more useful intelligent systems with less waste.
Conclusion
With the fast growth of AI, energy efficiency has become one of the most important issues in computing. GPUs have helped drive AI forwards, however this has led researchers to look into alternative solutions to reduce the cost of computation.
Brain inspired AI can be a great solution. Technologies such as neuromorphic computing, spiking neural networks and event based computing use principles that help biological systems process information efficiently. This can help develop intelligent devices with less power consumption, faster responses and can work well in environments with limited computing resources.
However there are still some issues.
The hardware isn’t readily available, it can be difficult to train, the tools aren’t mature and we need to be fair with comparisons.
The future of AI may not be to move away from GPUs completely. However we may move away from using just one type of computing system. By combining traditional accelerators with brain inspired hardware and energy aware algorithms, researchers may be able to create more capable and sustainable AI systems.
Ultimately we want to make more intelligent systems in an efficient way.
Keywords: Brain-Inspired AI, Neuromorphic Computing, Energy-Efficient AI, Spiking Neural Networks, GPUs, Sustainable Artificial Intelligence, Edge AI.