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 (Yole). And he adds: “Many similarities point to the idea that such a paradigm shift could happen quickly.”
Several years ago, the biggest obstacle preventing the 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 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. 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…
Yole explores today the computing and deep learning world with an imaging focus. The new report, Neuromorphic Sensing & Computing delivers an in-depth understanding of the neuromorphic landscape with key technology trends, competitive landscape, market dynamics and segmentation per application. It presents key technical insights and analysis regarding future technology trends and challenges. This analysis is at the cross road of two industries covered by Yole’s analysts: imaging and software & computing.
How will this industry evolve? Who are the companies to watch? What is the status of their development? Yole proposes you today an overview of the neuromorphic sensing ecosystem… Full story
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