Projektbeschreibung
Ein maschineller Schritt nach vorne für die Technologie autonomer Fahrzeuge
Angesichts der jüngsten Fortschritte beim maschinellen Lernen ist vielen daran gelegen, diese Technologie auch in anderen Bereichen einzusetzen. Zu den Bereichen mit großem diesbezüglichem Potenzial zählt die Industrie für autonome Fahrzeuge. Selbstfahrende Autos könnten bedeutende Vorteile für den Transportsektor bieten, indem sie das Verkehrsaufkommen, die Anzahl an Unfällen, Reisekosten und Reisedauern senken. Bedauerlicherweise verbrauchen die derzeitigen Deep-Learning-Aufgaben, die für einen effizienten und sicheren Betrieb autonomer Fahrzeuge erforderlich sind, zu viel Strom und Rechenleistung für aktuelle Modelle. Das EU-finanzierte Projekt Hailo-8 hat vor, eine alternative Lösung namens Hailo-8 zu entwickeln, die platz-, strom- und kostensparender ist und gleichzeitig weitere Fortschritte der Technologie autonomer Fahrzeuge ermöglicht.
Ziel
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.
Wissenschaftliches Gebiet
Not validated
Not validated
- engineering and technologymechanical engineeringvehicle engineeringautomotive engineeringautonomous vehicles
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringcomputer hardwarecomputer processors
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- natural sciencescomputer and information sciencesdata sciencedata processing
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
Programm/Programme
Thema/Themen
Aufforderung zur Vorschlagseinreichung
Andere Projekte für diesen Aufruf anzeigenUnterauftrag
H2020-SMEInst-2018-2020-2
Finanzierungsplan
SME-2 - SME instrument phase 2Koordinator
6789139 TEL-AVIV
Israel
Die Organisation definierte sich zum Zeitpunkt der Unterzeichnung der Finanzhilfevereinbarung selbst als KMU (Kleine und mittlere Unternehmen).