Neuromorphic Computing Could Play a Major Role in Artificial Intelligence

A Recent Study by Applied Brain Research

CHICAGO, December 14, 2018 /PRNewswire/ --

On 6th December 2018, Applied Brain Research Inc. (Canada), a neuromorphic computing software developing company, released the results of a study that evaluated the performance of their Nengo Deep Learning Toolkit, running an audio keyword spotting deep learning network on Intel's Loihi neuromorphic research chip and compared the energy efficiency to traditional hardware.

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The benchmarking results show that Nengo DL on Intel Loihi uses 38x less energy per inference than an architecturally identical network running on an NVIDIA Quadro K4000 GPU. The study also compared the dynamic energy cost per inference performance of the same deep network on several other platforms. In each case, the Nengo DL on Loihi network consumed significantly less power. In comparison, the NVIDIA Jetson TX1 edge GPU consumed 7.3x more energy, the Intel Xeon E5-2630 CPU consumed 8.2x more energy, and the Movidius Neural Compute Stick consumed 1.9x more energy.

MarketsandMarkets Viewpoint: 

According to the Point Of View of Sachin Garg - AVP : Semiconductor and Electronics, at MarketsandMarkets, The benchmark result indicates that Nengo DL on Loihi outperforms other platforms on an energy cost per inference basis while maintaining near-equivalent inference accuracy, which indicates that neuromorphic computing will have a major role to play in Artificial Intelligence. This development is an important breakthrough towards the commercialization of neuromorphics. Currently, AI relies on GPU acceleration for training and inference. However, the latest study suggests that neuromorphic computing, once commercialized, could be more efficient for real-time AI processing than GPU and CPU platforms.

Neuromorphic Computing Market: 

Neuromorphic computing involves the use of the functional principles of a human brain to help design and fabricate an artificial system. Neuromorphic circuits are inspired by the nervous system and are useful components in artificial perception/action systems, which also help in verifying neuro-physiological models. The major drivers for the growth of the neuromorphic computing market include new ways of computation possible due to the end of Moore's law; requirement of better performing ICs for computation speed, power consumption, packaging density; and increase in demand for artificial intelligence and machine learning.