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Intel Introduces New and Powerful Neuromorphic Research System for AI Workloads
March 19, 2020 News

 

Intel announced the readiness of Pohoiki Springs, its latest and most powerful neuromorphic research system providing the computational capacity of 100 million neurons. The cloud-based system will be made available to members of the Intel Neuromorphic Research Community (INRC), extending their neuromorphic work to solve larger, more complex problems.

“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 Neuromorphic Computing Lab.

 

What is it: Pohoiki Springs is a data centre rack-mounted system and is Intel’s largest neuromorphic computing system developed to date. It integrates 768 Loihi neuromorphic research chips inside a chassis the size of five standard servers.

Loihi processors take inspiration from the human brain. Loihi can process certain demanding workloads up to 1,000 times faster and 10,000 times more efficiently than conventional processors like the brain. Pohoiki Springs is the next step in scaling this architecture to assess its potential to solve a wide range of computationally difficult problems, not just artificial intelligence (AI). Intel researchers believe the extreme parallelism and asynchronous signaling of neuromorphic systems may provide significant performance gains at dramatically reduced power levels compared with the most advanced conventional computers available today.

 

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, despite having brains with well under 1 million neurons, can visually track objects and navigate and avoid obstacles in real time.

Similarly, Intel’s smallest neuromorphic system, Kapoho Bay, comprises two Loihi chips with 262,000 neurons and supports a variety of real-time edge workloads. Intel and INRC researchers have demonstrated the ability for Loihi to recognize gestures in real time, read braille using novel artificial skin, orient direction using learned visual landmarks and learn new odor patterns – all while consuming tens of milliwatts of power. These small-scale examples have so far shown excellent scalability, with larger problems running faster and more efficiently on Loihi compared with conventional solutions. This mirrors the scalability of brains found in nature, from insects to human brains.

Pohoiki Springs increases Loihi’s neural capacity to the size of a small mammal brain, with 100 million neurons, which is a major step on the path to supporting much larger and more sophisticated neuromorphic workloads. The system lays the foundation for an autonomous, connected future, which will require new approaches to real-time, dynamic data processing.

 

How It will be Used: Pohoiki Springs, Intel’s neuromorphic systems, are still in the research phase and are not intended to replace conventional computing systems. Instead, they provide a tool for researchers to develop and characterise new neuro-inspired algorithms for real-time processing, problem solving, adaptation and learning. INRC members will access and build applications on Pohoiki Springs via the cloud using Intel’s Nx SDK and community-contributed software components.

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.
  • Optimisation 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.
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