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Bringing Edge Artifical Intelligence to Life

Published on 27 January 2021

Bringing Edge Artifical Intelligence to Life

Artificial Intelligence is no longer an abstract concept, it already fuels our everyday life with communication tools (e.g Google 'Smart Compose' feature, Siri, etc). Tomorrow, AI will play a greater and perhaps a more important societal role, predicting and assessing our health risks, providing customer support, easing trafic congestion. Cars will be packed with AI features, including speech and gesture recognition, eye tracking, and so on. Some of these applications will require unprecedent responsiveness (e.g. braking systems). In such a context, the Cloud only will not do. AI will also need to be supported locally, meaning at the Edge. Algorithms will need to be processed locally, directly on the hardware device.

With the General Data Protection Regulation in mind, European Commission for Internal Market set the challenge:  80% of data will need to be processed directly within the hardware over the next five years. Currently, only 20% are being supported locally.  CEA-Leti's experts mission will consist of combining high performance computing capacity with low energy consumption in ultra-miniaturized systems, at low cost. Systems supporting AI at the edge will need to be able to perform thousands of billions of operations per second, consuming a single Watt or even less.

Bridging the Gap: Privacy and Efficiency

Because connection to the Cloud or any kind of networks won't be required anymore, systems will be fully independent, able to process data and take decisions by themselves.  Systems will be able to operate independently, translating into increased cybersecurity.  The absence of back and forth between the object and a distance platform will help keeping citizen's data safe and private. Beyond privacy, Edge AI addresses various current technical challenges, by offering: 

  • Energy sobriety: more than 90% of data sent to the Cloud is never used again. Beyond trim waste, it has become vital to drastically reduce data transfers and cut on data storage cost
  • Greater autonomy with fully independent systems. In fact, complex decisions without depending on the Cloud are key for medical devices providing continuous treatment (i.e diabetics).
  • Continuous safe operation: Applications such as autonomous vehicles or production lines will require continuous safe operation
  • Low latency:  A current data ride from a sensor to the Cloud located at 1,500+ km and back takes about 10ms. Edge AI will help reduce latency to 1 ms or less. 

To go further with tutorials IA Subscribe now to the CEA-Leti YouTube channel  




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