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Intel’s neuromorphic processors are smarter than conventional graphics and central processors
04.12.2021 [19:39],
Gennady Detinich

Intel has been building the Intel Neuromorphic Research Community (INRC) for two years, which is studying the application of the company’s neuromorphic processors. Today, for the first time in research, the company is proving the superiority of neuromorphic processors in the field of machine learning over graphics and central processing units with numbers in hand. Systems on NPU Intel Loihi learn faster and more efficiently, which opens up a lot of opportunities for them.

Currently, there are no universal tests that could help objectively compare the effectiveness of machine learning on classical computing platforms and neuromorphic platforms. Therefore, while Intel and partners propose to compare the efficiency of systems based on speed and efficiency in real training scenarios for a particular platform.

For example, Accenture found that when recognizing voice commands, the Intel Loihi chip does it with the same accuracy as a “standard GPU”, but responds to speech 200ms faster and performs recognition with 1000 times higher efficiency. A similar situation with gesture recognition. Using an Intel camera, Loihi learns gestures in just a few demonstrations, which can be used to control smart homes or public terminals.

Retail Researchers Appreciate Loihi’s Superiority for Image-Based Product Search. They found that the Loihi neuromorphic processor generates image feature vectors with more than three times the power efficiency of traditional CPUs and GPUs. Earlier this year, Intel showed Loihi’s ability to search feature vectors in databases with millions of images 24x faster and 30x less power efficient than x86-compatible processors.

When solving optimization and search problems, it turned out that NPU Loihi can solve problems more than 1000 times more efficiently and 100 times faster than traditional processors. This is especially important for empowering peripheral automation skills. For example, enabling drones to make complex navigation decisions in real time. When implementing the Loihi platform at a data center scale, these skills can be used to optimize logistics or, for example, to manage train traffic.

Robotics specialist Rutgers has determined that its Loihi solutions, without sacrificing performance, require 75 times less power than conventional mobile GPU implementations. In turn, a team of researchers from ETH Zurich found that the Loihi processor performs the task of tracking the horizon with a drone platform 1000 times better in terms of combined efficiency and speed than conventional computing platforms.

Obviously, these are not the first and not the last reports on the achievements of the Intel Loihi platform. Moreover, the company is preparing the next generation of neuromorphic processors based on the experience gained. Neuromorphic processors promise to grow wiser before our eyes, and their algorithms will be more and more perfect.

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