Intel breaks a billion neurons for world’s largest neuromorphic computing system
Intel has developed the world’s largest neuromorphic computing system with over 1bn neurons.
The Hala Point neuromorphic computing system, initially deployed at Sandia National Laboratories, uses Intel’s Loihi 2 processor which was launched back in 2019. It contains 1.15 billion neurons in 1,152 Loihi 2 processors produced on Intel 4 process node in a six-rack-unit data centre chassis. 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
Characterization shows it can support up to 20 quadrillion operations per second, or 20 petaops, with an efficiency exceeding 15 trillion 8-bit operations per second per watt (TOPS/W) when executing conventional deep neural networks.
The system will be used for research into future brain-inspired artificial intelligence (AI), and tackles the challenges of power efficiency in today’s AI data centre systems.
Compared to the first-generation large-scale research system, Pohoiki Springs, Hala Point provides 10 times the neuron capacity and up to 12 times higher performance and can run mainstream AI workloads.
This comes from the massively parallelized fabric which enables 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.
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Applied to bio-inspired spiking neural network models, the Hala Point neuromorphic computing 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.
The 15TOPS/W figure comes from using 10:1 sparse connectivity and event-driven activity 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.
“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,” said Mike Davies, director of the Neuromorphic Computing Lab at Intel Labs.
The neuromorphic computing architecture could enable future real-time continuous learning for AI applications such as scientific and engineering problem-solving, logistics, smart city infrastructure management, large language models (LLMs) and AI agents.
“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 and Intel anticipates that such lessons will lead to practical advancements, such as the ability for LLMs to learn continuously from new data, avoiding the cost and power overhead of training AI frameworks.
Ericsson Research is also using the Loihi 2 chip to optimize telecom infrastructure efficiency.
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