Yole’s view on neuromorphics: a boost for computional efficiency

October 02, 2019 //By Julien Happich
neuromorphics
Today, the neuromorphic approach still occupies the ‘‘curio cabinet”. “Many are prophesying the advent of neuromorphic approaches in the same way deep learning techniques were wrongfully dismissed – until they ended up reigning”, explains Pierre Cambou, Principal Analyst, Imaging at Yole Développement.

 

“Many similarities point to the idea that such a paradigm shift could happen quickly” Cambou adds.
Several years ago, the biggest obstacle preventing the Deep Neural Network (DNN) approach from performing its best was the lack of suitable hardware to support DNN’s innovative software advances. Today, the same is true for neuromorphic technology – but as the first Spiking Neural Network (SNN) chips roll out, the first beachhead markets are ready to fuel growth. The initial markets are industrial and mobile, mainly for robotic revolution and real-time perception.


Neuromorphic sensing & computing: 2018 step by step
overview towards artificial intelligence. (Source. Neuromorphic
Sensing & Computing 2019 report, Yole Développement)

Within the next decade, the availability of hybrid in-memory computing chips should unlock the automotive market, which is desperate for a mass-market AD technology.
Neuromorphic sensing and computing could be the magic bullet for these markets, solving most of AI’s current issues while opening new perspectives in the decades to come, says Yole.
In its new report, Neuromorphic Sensing & Computing, the market research firm explores the computing and deep learning world with an imaging focus, delivering an in-depth understanding of the neuromorphic landscape with key technology trends, competitive landscape, market dynamics and segmentation per application.

Since 2012, deep learning techniques have proven their superiority in the AI space. These techniques have spurred a giant leap in performance, and have been widely adopted by the industry.

Recently, we have witnessed a race for the development of new chips specialized for deep-learning training and inference, either for high-performance computing, servers, or edge applications”, asserts Yohann Tschudi, Technology & Market Analyst, Computing & Software at Yole. “These chips use the existing semiconductor paradigm based on Moore’s Law. And while it is technically possible to manufacture chips capable of performing hundreds of Tops to serve today’s AI application space, the desired computing power is still well below expectations.”
Consequently, an arms race is ongoing, centring on the use of ‘’brute force computing” to address computing power requirements. The technology node currently used is already at 7nm, and full wafer chips have emerged. Room for improvement appears small, and relying solely on the Moore’s Law paradigm is creating several uncertainties.


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