Breaking News

Samsung Display to Introduce First 90Hz OLED Laptop Display addlink launch P20 Portable SSD speed of up to 1050MB/s aKASA announces RGB controllers the new SOHO and VEGAS XL A Brief History of the Galaxy S Series’ Camera Technologies Samsung Introduces the 870 EVO SATA SSD Series

logo

  • Share Us
    • Facebook
    • Twitter
  • Home
  • Home
  • News
  • Reviews
  • Forum
  • Legacy
  • About
    • Submit News

    • Contact Us
    • Privacy

    • Promotion
    • Advertise

    • RSS Feed
    • Site Map

Search form

Samsung Introduces A High-Speed, Low-Power NPU Solution for AI Deep Learning

Samsung Introduces A High-Speed, Low-Power NPU Solution for AI Deep Learning

Enterprise & IT Jul 2,2019 0

Samsung has presented an On-Device AI algorithm that says it is over 4 times lighter and 8 times faster than existing algorithms.

Deep learning algorithms are a core element of artificial intelligence (AI) as they are the processes by which a computer is able to think and learn like a human being does. A Neural Processing Unit (NPU) is a processor that is optimized for deep learning algorithm computation, designed to efficiently process thousands of these computations simultaneously.

Samsung last month announced its goal to strengthen its leadership in the global system semiconductor industry by 2030 through expanding its proprietary NPU technology development. The company recently delivered an update to this goal at the conference on Computer Vision and Pattern Recognition (CVPR), one of the top academic conferences in computer vision fields.

This update is the company’s development of its On-Device AI lightweight algorithm, introduced at CVPR with a paper titled “Learning to Quantize Deep Networks by Optimizing Quantization Intervals With Task Loss”. On-Device AI technologies directly compute and process data from within the device itself. Over 4 times lighter and 8 times faster than existing algorithms, Samsung’s latest algorithm solution is improved from previous solutions and has been evaluated to be key to solving potential issues for low-power, high-speed computations.

Samsung Advanced Institute of Technology (SAIT) has announced that they have successfully developed On-Device AI lightweight technology that performs computations 8 times faster than the existing 32-bit deep learning data for servers. By adjusting the data into groups of under 4 bits while maintaining accurate data recognition, this method of deep learning algorithm processing is simultaneously much faster and much more energy efficient than existing solutions.

Samsung’s new On-Device AI processing technology determines the intervals of the significant data that influence overall deep learning performance through ‘learning’. This ‘Quantization Interval Learning (QIL)’ retains data accuracy by re-organizing the data to be presented in bits smaller than their existing size. SAIT ran experiments that successfully demonstrated how the quantization of an in-server deep learning algorithm in 32 bit intervals provided higher accuracy than other existing solutions when computed into levels of less than 4 bits.

When the data of a deep learning computation is presented in bit groups lower than 4 bits, computations of ‘and’ and ‘or’ are allowed, on top of the simpler arithmetic calculations of addition and multiplication. This means that the computation results using the QIL process can achieve the same results as existing processes can while using 1/40 to 1/120 fewer transistors.

As this system therefore requires less hardware and less electricity, it can be mounted directly in-device at the place where the data for an image or fingerprint sensor is being obtained, ahead of transmitting the processed data on to the necessary end points.

This technology will help develop Samsung’s system semiconductor capacity as well as strengthening one of the core technologies of the AI era – On-Device AI processing. Differing from AI services that use cloud servers, On-Device AI technologies directly compute data all from within the device itself.

On-Device AI technology can reduce the cost of cloud construction for AI operations since it operates on its own and provides quick and stable performance for use cases such as virtual reality and autonomous driving. Furthermore, On-Device AI technology can save personal biometric information used for device authentication, such as fingerprint, iris and face scans, onto mobile devices safely.

A core feature of On-Device AI technology is its ability to compute large amounts of data at a high speed without consuming excessive amounts of electricity. Samsung’s first solution to this end was the Exynos 9 (9820), introduced last year, which featured a proprietary Samsung NPU inside the mobile System on Chip (SoC). This product allows mobile devices to perform AI computations independent of any external cloud server.

Many companies are turning their attention to On-Device AI technology. Samsung plans to applying this algorithm not only to mobile SoC, but also to memory and sensor solutions in the near future.

Tags: Neural Networksdeep learningArtificial IntelligenceSAMSUNG
Previous Post
Automotive and Mobility Companies Publish Framework for Safe Automated Driving Systems
Next Post
LG G8S THINQ Combines Best of G Series With Features Popular in Global Markets

Related Posts

  • A Brief History of the Galaxy S Series’ Camera Technologies

  • Samsung Introduces the 870 EVO SATA SSD Series

  • Samsung Brings the Ultimate Gaming Experience to 2021 Neo QLED and QLEDs

  • Samsung Announces S21 series with new Galaxy Buds Pro

  • Samsung Announces Exynos 2100 5G chipset

  • Samsung Introduces Latest Innovations for a Better Normal at CES 2021

  • Neo QLED: Samsung televisions use mini LEDs for illumination

  • Premium Comes Standard with Galaxy Chromebook 2 – World’s First QLED Chromebook

Latest News

Samsung Display to Introduce First 90Hz OLED Laptop Display
Enterprise & IT

Samsung Display to Introduce First 90Hz OLED Laptop Display

addlink launch P20 Portable SSD speed of up to 1050MB/s
PC components

addlink launch P20 Portable SSD speed of up to 1050MB/s

aKASA announces RGB controllers the new SOHO and VEGAS XL
PC components

aKASA announces RGB controllers the new SOHO and VEGAS XL

A Brief History of the Galaxy S Series’ Camera Technologies
Smartphones

A Brief History of the Galaxy S Series’ Camera Technologies

Samsung Introduces the 870 EVO SATA SSD Series
PC components

Samsung Introduces the 870 EVO SATA SSD Series

Popular Reviews

CeBIT 2005

CeBIT 2005

Zidoo Z9S 4K Media Player review

Zidoo Z9S 4K Media Player review

CeBIT 2006

CeBIT 2006

LiteOn iHBS112 review

LiteOn iHBS112 review

Club3D HD3850

Club3D HD3850

Crucial P1 NVMe 1TB SSD review

Crucial P1 NVMe 1TB SSD review

Hitachi DZ-MV100A DVD Camcorder

Hitachi DZ-MV100A DVD Camcorder

Toshiba Exceria M303 64GB and M501 Exceria Pro 64GB MicroSDXC review

Toshiba Exceria M303 64GB and M501 Exceria Pro 64GB MicroSDXC review

  • Home
  • News
  • Reviews
  • Forum
  • Legacy
  • About
    • Submit News

    • Contact Us
    • Privacy

    • Promotion
    • Advertise

    • RSS Feed
    • Site Map
  • About
  • Privacy
  • Contact Us
  • Promotional Opportunities @ CdrInfo.com
  • Advertise on out site
  • Submit your News to our site
  • RSS Feed