Opendata, web and dolomites

Reexen SIGNED

Ultra-low cost & ultra-high efficiency AI processor for enabling fast and cost-effective deployment of edge-computing applications

Total Cost €

0

EC-Contrib. €

0

Partnership

0

Views

0

 Reexen project word cloud

Explore the words cloud of the Reexen project. It provides you a very rough idea of what is the project "Reexen" about.

obtains    suitable    solution    eliminating    battery    finance    prototyping    tops    area    cloud    health    processor    100    chip    supporting    minimum    highest    storage    speech    image    ai    semiconductor    gaming    executing    capacity    requiring    company    latency    accuracy    extremely    algorithms    efficiency    appears    transversal    reexen    size    audio    successful    reducing    attract    contexts    memory    additional    job    distance    core    edge    industry    mixed    energy    computational    10ms    circuits    services    incurring    networks    aligns    context    conversions    generation    robotics    neural    completely    deep    throughput    limited    suboptimal    international    recognition    power    mobile    exploded    designed    communication    financing    fabrication    business    myriad    talent    ones    superior    dnns    efficient    sensors    consumption    lt    running    signal    complexity    ultra    breakthrough    data    time    smart    processors    achieves    network    technologies    nucleus    dnn    fast    maximum   

Project "Reexen" data sheet

The following table provides information about the project.

Coordinator
CYBERTRON TECH GMBH 

Organization address
address: LIMMATQUAI 106
city: ZURICH
postcode: 8001
website: n.a.

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Switzerland [CH]
 Total cost 71˙429 €
 EC max contribution 50˙000 € (70%)
 Programme 1. H2020-EU.3. (PRIORITY 'Societal challenges)
2. H2020-EU.2.3. (INDUSTRIAL LEADERSHIP - Innovation In SMEs)
3. H2020-EU.2.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies)
 Code Call H2020-SMEInst-2018-2020-1
 Funding Scheme SME-1
 Starting year 2020
 Duration (year-month-day) from 2020-01-01   to  2020-04-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    CYBERTRON TECH GMBH CH (ZURICH) coordinator 50˙000.00

Map

 Project objective

'Since the breakthrough application of Deep Neural Networks algorithms (DNNs) to speech and image recognition, the number of applications that use DNNs has exploded, achieving the highest accuracy in a myriad of contexts (health, robotics, finance, gaming, etc.). However, their superior accuracy comes at the cost of high computational complexity. Current approaches to solve this challenge are cloud-based, incurring in high power consumption and high latency, given their communication needs. Although cloud approaches are suitable for some context, they are suboptimal for real-time applications running on embedded or mobile devices (with limited battery capacity and requiring fast responses). REEXEN appears to bring a solution to this challenge: an extremely efficient AI processor (a semiconductor chip) specifically designed for supporting DNN-based edge applications. By exploiting state-of-the-art semiconductor technologies in mixed-signal circuits and in-memory processing, REEXEN obtains the best power-efficiency when executing DNN algorithms, in terms of maximum throughput per energy unit consumption (30 TOPs/W). By reducing the 'distance' between data generation (sensors), data storage (memory) and data processing (core processor or nucleus), and by eliminating A/D conversions, REEXEN also achieves minimum latency (<10ms) and fabrication area, thus also reducing the overall cost of production. REEXEN completely aligns with the EU approach to AI, as an enabling technology that will allow the development of current industry-transversal smart services and the implementation of future new ones. Our company is 100% focused on developing next generation of ultra-low power neural network processors. From the successful results of our early prototyping for audio applications, REEXEN project will attract the best talent and additional financing to build the business around our technology and increase our company size, international presence and job generation.'

Are you the coordinator (or a participant) of this project? Plaese send me more information about the "REEXEN" project.

For instance: the website url (it has not provided by EU-opendata yet), the logo, a more detailed description of the project (in plain text as a rtf file or a word file), some pictures (as picture files, not embedded into any word file), twitter account, linkedin page, etc.

Send me an  email (fabio@fabiodisconzi.com) and I put them in your project's page as son as possible.

Thanks. And then put a link of this page into your project's website.

The information about "REEXEN" are provided by the European Opendata Portal: CORDIS opendata.

More projects from the same programme (H2020-EU.3.;H2020-EU.2.3.;H2020-EU.2.1.)

Magnesys (2019)

Efficient filtering of metallic impurities in food processing

Read More  

CAARESYS (2019)

CAARESYS: vehicle passenger monitoring system based on contactless low emission radio frequency radar.

Read More  

ECOBIOMASS (2019)

Achieving unique wines through an efficient production process

Read More