Intel Scales Neuromorphic Research System to 100 Million Neurons
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A close-up shows an Intel Nahuku board, each of which contains eight to 32 Intel Loihi neuromorphic research chips. Intel’s latest neuromorphic computing system,
“Pohoiki Springs scales up our Loihi neuromorphic research chip by more than 750 times, while operating at a power level of under 500 watts. The system enables our research partners to explore ways to accelerate workloads that run slowly today on conventional architectures, including high-performance computing (HPC) systems.”
–Mike Davies, director of Intel’s
What It is:
Loihi processors take inspiration from the human brain. Like the brain, Loihi can process certain demanding workloads up to 1,000 times faster and 10,000 times more efficiently than conventional processors.
What the Opportunity for Scale is: In the natural world even some of the smallest living organisms can solve remarkably hard computational problems. Many insects, for example, can visually track objects and navigate and avoid obstacles in real time, despite having brains with well under 1 million neurons.
Similarly, Intel’s smallest neuromorphic system,
With 100 million neurons,
How It will be Used: Intel’s neuromorphic systems, such as
Examples of promising, highly scalable algorithms being developed for Loihi include:
- Constraint satisfaction: Constraint satisfaction problems are present everywhere in the real world, from the game of sudoku to airline scheduling, to package delivery planning. They require evaluating a large number of potential solutions to identify the one or few that satisfy specific constraints. Loihi can accelerate such problems by exploring many different solutions in parallel at high speed.
- Searching graphs and patterns: Every day, people search graph-based data structures to find optimal paths and closely matching patterns, for example to obtain driving directions or to recognize faces. Loihi has shown the ability to rapidly identify the shortest paths in graphs and perform approximate image searches.
- Optimization problems: Neuromorphic architectures can be programmed so that their dynamic behavior over time mathematically optimizes specific objectives. This behavior may be applied to solve real-world optimization problems, such as maximizing the bandwidth of a wireless communication channel or allocating a stock portfolio to minimize risk at a target rate of return.
About Neuromorphic Computing: Traditional general-purpose processors, like CPUs and GPUs, are particularly skilled at tasks that are difficult for humans, such as highly precise mathematical calculations. But the role and applications of technology are expanding. From automation to AI and beyond, there is a rising need for computers to operate more like humans, processing unstructured and noisy data in real time, while adapting to change. This challenge motivates new and specialized architectures.
Neuromorphic computing is a complete rethinking of computer architecture from the bottom up. The goal is to apply the latest insights from neuroscience to create chips that function less like traditional computers and more like the human brain. Neuromorphic systems replicate the way neurons are organized, communicate and learn at the hardware level. Intel sees Loihi and future neuromorphic processors defining a new model of programmable computing to serve the world’s rising demand for pervasive, intelligent devices.