Neuromorphic Brain-Like Chips Promise Major Breakthrough in AI Energy Efficiency

Brain-like computer chips, known as neuromorphic processors, are moving from research labs into real products and could cut the energy use of artificial intelligence systems by orders of magnitude, according to new deals, prototypes, and early performance data released in 2024 and 2025.

As data centers consume more electricity than some countries and AI demand accelerates, chipmakers and researchers say neuromorphic chips could cut AI energy, and hardware could reduce power use by 10 to 100 times in many tasks and, in best-case tests, up to 1,000 times compared with today’s processors.

Why AI’s Power Hunger Is Becoming a Crisis

Global data centers are already major power users. Recent figures from the International Energy Agency show that data centers worldwide consumed around 415 terawatt-hours (TWh) of electricity in 2024. That is roughly 1.5% of total global electricity use and more than the annual consumption of many mid-sized countries.

The IEA projects that data center demand could more than double to about 945 TWh by 2030 if current trends in cloud, video streaming, and AI continue. A large share of this growth is expected to come from AI training and inference as models become larger and more widely deployed.

In the United States, data centers consumed an estimated 183 TWh of electricity in 2024, more than 4% of national power use and roughly equivalent to Pakistan’s entire annual electricity demand. Forecasts suggest this could rise to around 426 TWh by 2030, an increase of about 133%.

Key data center power figures

AI is a major driver of this growth. Training a single large language model at the frontier scale can emit on the order of 500 tonnes of CO₂, depending on the data center’s energy mix. But experts warn that the real long-term problem is not training alone. Once deployed, running these models for billions of queries across search, chatbots, recommendation systems, and autonomous systems can account for the majority of their lifetime energy use.

This is the context in which neuromorphic, “brain-like” chips are gaining attention.

What Are Brain-Like Chips and How Do They Work?

Neuromorphic chips are designed to mimic how the human brain processes information. Instead of separating memory and computation, as traditional CPUs and GPUs do, they bring them together in a way that resembles networks of neurons and synapses.

Many neuromorphic processors use spiking neural networks, where information is transmitted as brief electrical “spikes,” similar to how biological neurons fire. They also use an approach called in-memory computing, where data is stored and processed in the same physical location. This reduces the need to constantly move data back and forth, which is one of the most energy-intensive parts of modern computing.

Some experimental devices, such as those based on memristors or magnetic tunnel junctions, even rely on ion dynamics or magnetic states rather than conventional electron-based switching. Early results suggest that artificial neurons built this way can fire using around a picojoule of energy per spike, far below typical digital circuits.

Conventional vs neuromorphic chips at a glance

The key promise is efficiency: by processing only relevant events and limiting data movement, neuromorphic chips can dramatically cut energy use for certain types of AI workloads, especially those involving sensor streams, pattern recognition, or control tasks.

New Deals Push Neuromorphic Hardware Toward Market

After years of research, 2024 and 2025 have brought several signs that neuromorphic chips are moving closer to commercial use in industry, defense, and consumer devices.

In December 2025, Netherlands-based startup Innatera announced a partnership with UK engineering consultancy 42 Technology. The aim is to integrate Innatera’s Pulsar neuromorphic microcontroller into real-world products, from industrial condition monitoring systems to consumer electronics. The Pulsar chip is designed to process sensor data at very low power levels, with company materials suggesting sub-milliwatt operation in some tasks.

In parallel, Australian company BrainChip has launched its Akida neuromorphic processor in the M.2 form factor, the same slot used for solid-state drives in many PCs. This makes it easier to plug neuromorphic hardware into existing edge and embedded systems. BrainChip also secured a contract with a major defense contractor working with the U.S. Air Force Research Laboratory, focused on applying neuromorphic techniques to radar signal processing.

Public research institutions are scaling up as well. Sandia National Laboratories has deployed a SpiNNaker2-based neuromorphic system capable of simulating around 175 million neurons, similar to the brain of a small mammal. It will be used to explore applications ranging from nuclear deterrence modeling to advanced AI algorithms.

Recent neuromorphic milestones

These steps signal a shift from pure laboratory demonstrations toward early commercial deployments. However, the market is still in its infancy compared with mainstream GPU and CPU ecosystems.

How Much Energy Could Brain-Like Chips Really Save?

The brain-like chips could slash AI energy use by up to 1,000 times, according to vendor and analyst comparisons in specific, controlled tests. Those results are attention-grabbing, but they do not yet represent typical, large-scale deployment conditions.

More modest but still striking gains have been demonstrated in peer-reviewed or publicly documented benchmarks.

• Intel’s Loihi 2 neuromorphic chip has shown up to around 100 times lower energy use and up to 50 times faster performance than traditional CPU/GPU setups on certain optimization and inference tasks.
• IBM’s NorthPole research chip, which tightly combines compute and memory on the same die, has recorded roughly 25 times better energy efficiency and more than 20 times higher speed than some conventional GPU and CPU platforms for image-recognition workloads.
• Innatera reports that its Pulsar device can, in some sensor-processing tasks, offer as much as 500 times lower energy use and 100 times lower latency than conventional edge AI accelerators.
• Mercedes-Benz research indicates that neuromorphic vision systems could cut compute energy for autonomous driving by up to 90% compared with current solutions.

In one widely cited comparison, a neuromorphic system based on Intel’s Loihi architecture reportedly delivered up to 1,000 times better energy efficiency and significantly lower latency than an Nvidia Jetson edge AI module for a specific type of state-space model. That figure underpins many “1,000x” headlines, but it is important to stress that it applies to a narrow class of workloads and a particular test setup.

Reported neuromorphic performance gains

Taken together, these results suggest that double-digit energy savings—10 to 100 times—for targeted workloads are realistic in the near term. The much larger “up to 1,000x” gains are best seen as upper-bound scenarios that may apply only under specific conditions.

From Training Rooms to Everyday Devices

Most of today’s AI energy use still comes from running models rather than training them. Once a large language model or vision system is deployed in search engines, apps, factories, or cars, the cumulative power used over years of inference can far exceed the energy needed for initial training.

Neuromorphic chips are particularly attractive for inference and on-device learning at the edge:

• They can run continuously on small batteries or energy-harvesting systems.
• They can process raw sensor data locally, reducing the need to send every signal to the cloud.
• Some designs support incremental, online learning without retraining a large model in a data center.

Researchers at the University of Texas at Dallas, for example, have demonstrated neuromorphic systems based on magnetic tunnel junctions that can learn patterns using far fewer training computations than conventional deep learning. At the University of Southern California, teams working with diffusive memristors have shown artificial neurons that closely mimic biological spiking behavior at extremely low energy per spike.

These technologies are still experimental, but they point to a future where many small AI tasks—recognizing gestures, monitoring vibrations, analyzing biosignals—could be handled by ultra-low-power chips embedded in everyday objects.

Training vs inference and AI energy

Market Outlook: Big Growth, Open Questions

Market research firms expect rapid growth in neuromorphic computing over the next decade, although estimates vary widely. One report values the global neuromorphic market at about 4.89 billion dollars in 2025 and projects it could reach more than 76 billion dollars by 2035, implying a compound annual growth rate of over 30%.

Some industry analyses forecast that neuromorphic processors could be present in a large share of Internet of Things sensor nodes by 2030, potentially approaching 40% in optimistic scenarios.

Others are more cautious, pointing to challenges such as:

• The need for new software tools and developer ecosystems.
• Compatibility with existing AI frameworks dominated by GPUs.
• Uncertainty about which neuromorphic architectures will become industry standards.
• Policy and regulatory scrutiny of AI energy use and emissions.

For now, neuromorphic chips are likely to appear first in niche areas where extreme energy efficiency and low latency are critical, such as industrial monitoring, defense, autonomous vehicles, and specialized edge devices. Broader use in mainstream data centers will depend on whether hardware, software, and algorithms can mature together.

Neuromorphic market and adoption snapshot

What It Means: Promise, But No Magic Wand Yet

Brain-like chips are emerging at a time when AI’s energy footprint is under intense scrutiny from policymakers, investors, and the public. The latest hardware results show that neuromorphic designs can deliver substantial efficiency gains for certain tasks, and in some cases, spectacular improvements compared with general-purpose processors.

However, the path from impressive laboratory benchmarks to sweeping reductions in global electricity use is long and uncertain. Real-world deployments will depend on business models, standards, software tools, and regulation, not just on chip physics.

For now, neuromorphic processors offer a clear message: there are technical ways to make AI far more efficient. Whether they will scale quickly enough to meaningfully bend the curve of data center energy demand will be one of the key technology questions of the coming decade.