Intel has built the world’s largest neuromorphic system. Code-named Hala Point, this large-scale neuromorphic system, initially deployed at Sandia National Laboratories, utilizes Intel’s Loihi 2 processor. 

Accordingly, it aims at supporting research for future brain-inspired artificial intelligence (AI), and tackles challenges related to the efficiency and sustainability of today’s AI. Hala Point advances Intel’s first-generation large-scale research system, Pohoiki Springs. It comes with architectural improvements to achieve over 10 times more neuron capacity and up to 12 times higher performance.

Mike Davies, director of the Neuromorphic Computing Lab at Intel Labs, said, “The computing cost of today’s AI models is rising at unsustainable rates. The industry needs fundamentally new approaches capable of scaling.”

“For that reason, we developed Hala Point, which combines deep learning efficiency with novel brain-inspired learning and optimization capabilities. We hope that research with Hala Point will advance the efficiency and adaptability of large-scale AI technology,” added Davies.

Leads to Practical Advancements

Hala Point is the first large-scale neuromorphic system to demonstrate state-of-the-art computational efficiencies on mainstream AI workloads. Essentially, it can support up to 20 quadrillion operations per second, or 20 petaops. This comes with an efficiency exceeding 15 trillion 8-bit operations per second per watt when executing conventional deep neural networks.

Thus, it rivals and exceeds levels achieved by architectures built on graphics processing units (GPU) and central processing units (CPU). Hala Point’s unique capabilities could enable future real-time continuous learning for AI applications such as scientific and engineering problem-solving. Moreover, it can suit logistics, smart city infrastructure management, large language models (LLMs), and AI agents.

Researchers at Sandia National Laboratories plan to use Hala Point for advanced brain-scale computing research. Specifically, the organization will focus on solving scientific computing problems in device physics, computer architecture, computer science, and informatics.

“Working with Hala Point improves our Sandia team’s capability to solve computational and scientific modeling problems. Conducting research with a system of this size will allow us to keep pace with AI’s evolution in fields ranging from commercial to defense to basic science,” said Craig Vineyard, Hala Point team lead at Sandia National Laboratories.

Currently, Hala Point is a research prototype that will advance the capabilities of future commercial systems. Intel anticipates that such lessons will lead to practical advancements, such as the ability for LLMs to learn continuously from new data. Moreover, such advancements promise to significantly reduce the unsustainable training burden of widespread AI deployments.

Requiring More Sustainable AI

Recent trendsin scaling up deep learning models to trillions of parameters have exposed daunting sustainability challenges in AI. Furthermore, these trends have highlighted the need for innovation at the lowest levels of hardware architecture.

For that reason, neuromorphic computing is a fundamentally new approach that draws on neuroscience insights. These integrate memory and computing with highly granular parallelism to minimize data movement.

In published results from this month’s International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Loihi 2 demonstrated orders of magnitude gains. Specifically, in the efficiency, speed, and adaptability of emerging small-scale edge workloads.

Advancing on its predecessor, Pohoiki Springs, with numerous improvements, Hala Point now brings neuromorphic performance and efficiency gains to mainstream conventional deep learning models, notably those processing real-time workloads such as video, speech, and wireless communications. For example, Ericsson Research is applying Loihi 2 to optimize telecom infrastructure efficiency, as highlighted at this year’s Mobile World Congress.

-18 April 2024-