Skip to main content

End-to-end hardware implementation of Artificial Neural Networks for Edge Computing in Autonomous Vehicles


Autonomous Vehicles (AVs) present a great opportunity for the transport sector to reduce accidents, traffic congestion, time of travel and travel costs. However, for effectiveness, AVs need to process large amounts of data collected by the vehicle sensors at the edge, which requires a very powerful processor capable of computing Deep Learning (DL) tasks. This is currently lacking in the market as evidenced by the inefficiencies in current processors in processing big data at the edge in real time. Most processors for edge computing are currently reliant on CPU and GPU architectures which are challenged by Deep Learning tasks. The processors have low computational capabilities which increases their latencies (processing times). This leads to heat dissipation problems and high power consumption. The processors are also rigged with complexities that raise development costs and the price of the processors. The processors are also not easily scalable, which makes it difficult for miniaturisation.

Hailo-Tech has developed Hailo-8, which is specifically designed to optimise Edge Computing processor capabilities to allow neural network deployment through enhancing processor computational efficiency, resulting in higher capacity within the constraints of an edge device. Hailo-8 meets the industry need of optimised edge data processing by providing a first-class ASIC micro-processor that is based on a completely new micro-architecture that can execute neural network based machine learning algorithms. Hailo-8 will provide AV owners with high computational efficiency (x1,000 compared to alternative solutions), giving an immediate response after data processing. Hailo-8 increases power efficiency by a factor of 100 and has better area and cost efficiency by a factor of 10 compared to other processors. To bring the disruptive device successfully to the market we need to further perform some technical and commercial activities which required an investment of €2.993,750 M.

Field of science

  • /social sciences/economics and business/business and management/commerce
  • /natural sciences/computer and information sciences/artificial intelligence/computational intelligence
  • /engineering and technology/mechanical engineering/vehicle engineering/automotive engineering/autonomous vehicle
  • /natural sciences/computer and information sciences/data science/big data
  • /natural sciences/computer and information sciences/artificial intelligence/machine learning/deep learning
  • /natural sciences/computer and information sciences/data science/data processing
  • /engineering and technology/electrical engineering, electronic engineering, information engineering/electronic engineering/computer hardware/computer processor
  • /natural sciences/computer and information sciences/artificial intelligence/machine learning

Call for proposal

See other projects for this call

Funding Scheme

SME-2 - SME instrument phase 2
Leaflet | Map data © OpenStreetMap contributors, Credit: EC-GISCO, © EuroGeographics for the administrative boundaries


94 Yigal Alon
6789139 Tel-aviv
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
EU contribution
€ 2 095 625