An AI engine on ASIC close to the sensor

Easics uses its expertise in system-on-chip design to develop small, low-power and affordable AI engines that run locally, close to your sensors. 

This results in a low and predictable latency and runs with ultra low-power consumption.  Easics’ embedded AI solutions integrate tightly with novel and existing sensors such as image sensors capturing light inside and outside the visible spectrum (such as hyperspectral and thermal infrared), 3D scanning laser (LiDAR), Time-of-Flight (ToF) sensors, radar, microscopy, ultrasound sensors, and microphones.
To create such innovative hardware, easics is developing the next generation of its embedded AI framework. This framework automatically generates hardware implementations of the deep neural networks that make your specific application smart. Easics maps these AI engines on custom ASIC technology and FPGA. The framework and technology offers enough flexibility in terms of different deep learning models and hardware to have a scalable AI engine that is ready is for the future.
AI people object detection
 

On chip block diagram

AI engine easics on sensor

 

features and applications

sensor ASIC AI engine

Why choose easics deep learning on ASIC: 

  • close to the sensor 
  • ultra low power 
  • Low latency 
  • Fast time to market to embed AI close to the sensor  
  • flexible integration on sensor 

Applications that can benefit from easics AI engine:

  • Quality inspection - Smart camera's - Robotics - Autonomous Driving:  
    • image sensors  and AI engine (convolutional neural networks - CNN)
  • Hearing aid devices and implants - smart speakers:
    • Audio sensor and AI engine (recurrent neural networks - RNN e.g., Long short-term memory - LSTM) 
  • Robotics and autonomous driving: 
    • Lidar and AI engine 
  • IoT, Prededictive maintenance and remote monitoring: 
    • audio, vibration and temperature and AI engine (Ultra-Low Power, Fast, Compact Deep Nets)
  • Any sensor application that you would like to discuss with us to add AI on chip
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