Intel Builds World’s Largest Neuromorphic System to Enable More Sustainable AI

What’s New: Today, Intel announced that it 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, 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, with architectural improvements to achieve over 10 times more neuron capacity and up to 12 times higher performance.

“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.”

The world’s largest and Intel’s most advanced neuromorphic system to date, Hala Point, contains 1.15 billion neurons for more sustainable AI. (Credit: Intel Corporation)

Hala Point, the world’s largest and Intel’s most advanced neuromorphic system to date, contains 1.15 billion neurons and packages 1,152 Loihi 2 processors produced on Intel 4 process node in a six-rack-unit data center chassis the size of a microwave oven. The system supports up to 1.15 billion neurons and 128 billion synapses distributed over 140,544 neuromorphic processing cores, consuming a maximum of 2,600 watts of power. It also includes over 2,300 embedded x86 processors for ancillary computations. (Credit: Intel Corporation)

Mike Davies is the director of Neuromorphic Research at Intel Labs. (Credit: Intel Corporation)

The world’s largest and Intel’s most advanced neuromorphic system to date, Hala Point, contains 1.15 billion neurons for more sustainable AI. (Credit: Intel Corporation)

The Intel Neuromorphic Research Team pose for a photo with Hala Point (from left): Patricio Martinez, platform hardware design engineer, Eduardo Quijano Centeno, lead platform hardware design engineer, Gerardo Peralta Francisco, platform hardware designer, and Leobardo Campos Macias, AI applied research scientist. (Credit: Intel Corporation)

Hala Point is the world’s largest neuromorphic system, with neuron capacity roughly equivalent to that of an owl brain. (Credit: Intel Corporation)

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About Hala Point: Loihi 2 neuromorphic processors, which form the basis for Hala Point, apply brain-inspired computing principles, such as asynchronous, event-based spiking neural networks (SNNs), integrated memory and computing, and sparse and continuously changing connections to achieve orders-of-magnitude gains in energy consumption and performance. Neurons communicate directly with one another rather than communicating through memory, reducing overall power consumption.

Hala Point packages 1,152 Loihi 2 processors produced on Intel 4 process node in a six-rack-unit data center chassis the size of a microwave oven. The system supports up to 1.15 billion neurons and 128 billion synapses distributed over 140,544 neuromorphic processing cores, consuming a maximum of 2,600 watts of power. It also includes over 2,300 embedded x86 processors for ancillary computations.

Hala Point integrates processing, memory, and communication channels in a massively parallelized fabric, providing a total of 16 petabytes per second (PB/s) of memory bandwidth, 3.5 PB/s of inter-core communication bandwidth, and 5 terabytes per second (TB/s) of inter-chip communication bandwidth. The system can process over 380 trillion 8-bit synapses and over 240 trillion neuron operations per second.

Applied to bio-inspired spiking neural network models, the system can execute its full capacity of 1.15 billion neurons 20 times faster than a human brain and up to 200 times faster rates at lower capacity. While Hala Point is not intended for neuroscience modeling, its neuron capacity is roughly equivalent to that of an owl brain or the cortex of a capuchin monkey.

Loihi-based systems can perform AI inference and solve optimization problems using 100 times less energy at speeds as much as 50 times faster than conventional CPU and GPU architectures¹. By exploiting up to 10:1 sparse connectivity and event-driven activity, early results on Hala Point show the system can achieve deep neural network efficiencies as high as 15 TOPS/W² without requiring input data to be collected into batches, a common optimization for GPUs that significantly delays the processing of data arriving in real-time, such as video from cameras. While still in research, future neuromorphic LLMs capable of continuous learning could result in gigawatt-hours of energy savings by eliminating the need for periodic re-training with ever-growing datasets.

What’s Next: The delivery of Hala Point to Sandia National Labs marks the first deployment of a new family of large-scale neuromorphic research systems that Intel plans to share with its research collaborators. Further development will enable neuromorphic computing applications to overcome power and latency constraints that limit AI capabilities’ real-world, real-time deployment.

Together with an ecosystem of more than 200 Intel Neuromorphic Research Community (INRC) members, including leading academic groups, government labs, research institutions and companies worldwide, Intel is working to push the boundaries of brain-inspired AI and progressing this technology from research prototypes to industry-leading commercial products over the coming years.

More context: Intel Labs

The Small Print:

¹ _{See “Efficient Video and Audio Processing with Loihi 2,” International Conference on Acoustics, Speech, and Signal Processing, April 2024, and “Advancing Neuromorphic Computing with Loihi: Survey of Results and Outlook,” Proceedings of the IEEE, 2021.}
² _{Characterization performed with a multi-layer perceptron (MLP) network with 14,784 layers, 2048 neurons per layer, 8-bit weights stimulated with random noise. The Hala Point implementation of the MLP network is pruned to 10:1 sparsity with sigma-delta neuron models providing 10 percent activation rates. Results as of testing in April 2024. Results may vary.}