Saturday, July 19, 2025
LBNN
  • Business
  • Markets
  • Politics
  • Crypto
  • Finance
  • Energy
  • Technology
  • Taxes
  • Creator Economy
  • Wealth Management
  • Documentaries
No Result
View All Result
LBNN

Researchers develop next-gen semiconductor technology for high-efficiency, low-power artificial intelligence

Simon Osuji by Simon Osuji
August 2, 2024
in Artificial Intelligence
0
Researchers develop next-gen semiconductor technology for high-efficiency, low-power artificial intelligence
0
SHARES
2
VIEWS
Share on FacebookShare on Twitter


"Smarter" semiconductor technology for training "smarter" artificial intelligence
Cross-point array structure and operation method of the three-terminal ECRAM device fabricated in this study (Top) Measurement results of the array of three-terminal-based electrochemical memory devices, demonstrating excellent characteristics in both cycle and device-to-device scatter, well above the requirements for training neural networks (Bottom). Credit: POSTECH

A research team has demonstrated that analog hardware using ECRAM devices can maximize the computational performance of artificial intelligence, showcasing its potential for commercialization. Their research has been published in Science Advances.

Related posts

Automakers Are Canceling Plans for New EVs. Here’s a List of What’s Been Killed So Far

Automakers Are Canceling Plans for New EVs. Here’s a List of What’s Been Killed So Far

July 19, 2025
AI is now part of our world. University graduates should know how to use it responsibly

AI is now part of our world. University graduates should know how to use it responsibly

July 19, 2025

The rapid advancement of AI technology, including applications like generative AI, has pushed the scalability of existing digital hardware (CPUs, GPUs, ASICs, etc.) to its limits. Consequently, there is active research into analog hardware specialized for AI computation.

Analog hardware adjusts the resistance of semiconductors based on external voltage or current and utilizes a cross-point array structure with vertically crossed memory devices to process AI computation in parallel. Although it offers advantages over digital hardware for specific computational tasks and continuous data processing, meeting the diverse requirements for computational learning and inference remains challenging.

To address the limitations of analog hardware memory devices, the research team, consisting of Professor Seyoung Kim from the Department of Materials Science and Engineering and the Department of Semiconductor Engineering and others focused on Electrochemical Random Access Memory (ECRAM), which manages electrical conductivity through ion movement and concentration.

Unlike traditional semiconductor memory, these devices feature a three-terminal structure with separate paths for reading and writing data, allowing for operation at relatively low power.

In their study, the team successfully fabricated ECRAM devices using three-terminal-based semiconductors in a 64×64 array. Experiments revealed that the hardware incorporating the team’s devices demonstrated excellent electrical and switching characteristics, along with high yield and uniformity.

Additionally, the team applied the Tiki-Taka algorithm, a cutting-edge analog-based learning algorithm, to this high-yield hardware, successfully maximizing the accuracy of AI neural network training computations.

Notably, the researchers demonstrated the impact of the “weight retention” property of hardware training on learning and confirmed that their technique does not overload artificial neural networks, highlighting the potential for commercializing the technology.

This research is significant because the largest array of ECRAM devices for storing and processing analog signals reported in the literature to date is 10×10. The researchers have now successfully implemented these devices on the largest scale, with varied characteristics for each device.

Professor Seyoung Kim of POSTECH said, “By developing large-scale arrays based on novel memory device technologies and developing analog-specific AI algorithms, we have identified the potential for AI computational performance and energy efficiency that far surpass current digital methods.”

More information:
Kyungmi Noh et al, Retention-aware zero-shifting technique for Tiki-Taka algorithm-based analog deep learning accelerator, Science Advances (2024). DOI: 10.1126/sciadv.adl3350

Provided by
Pohang University of Science and Technology

Citation:
Researchers develop next-gen semiconductor technology for high-efficiency, low-power artificial intelligence (2024, August 1)
retrieved 1 August 2024
from https://techxplore.com/news/2024-08-gen-semiconductor-technology-high-efficiency.html

This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no
part may be reproduced without the written permission. The content is provided for information purposes only.





Source link

Previous Post

I woke up a rich man and slept a pauper – Kieni MP Njoroge Wainaina

Next Post

Marie-Therese Johnson: A key player in Dominica’s CBIU success

Next Post
Marie-Therese Johnson: A key player in Dominica’s CBIU success

Marie-Therese Johnson: A key player in Dominica’s CBIU success

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

RECOMMENDED NEWS

How Bitcoin’s road to new ATH was reflected in on-chain data

How Bitcoin’s road to new ATH was reflected in on-chain data

1 year ago
Yellowcard Leads Africa’s Crypto Revolution, Transforming Financial Systems for All

Yellowcard Leads Africa’s Crypto Revolution, Transforming Financial Systems for All

6 months ago
NASA Engineers Are Racing to Fix Voyager 1

NASA Engineers Are Racing to Fix Voyager 1

1 year ago
BRICS Developing Effective Mechanisms for Global Financial Control

BRICS Developing Effective Mechanisms for Global Financial Control

2 years ago

POPULAR NEWS

  • Ghana to build three oil refineries, five petrochemical plants in energy sector overhaul

    Ghana to build three oil refineries, five petrochemical plants in energy sector overhaul

    0 shares
    Share 0 Tweet 0
  • When Will SHIB Reach $1? Here’s What ChatGPT Says

    0 shares
    Share 0 Tweet 0
  • The world’s top 10 most valuable car brands in 2025

    0 shares
    Share 0 Tweet 0
  • Top 10 African countries with the highest GDP per capita in 2025

    0 shares
    Share 0 Tweet 0
  • Tanzania’s natural gas sector goes global with Dubai deal

    0 shares
    Share 0 Tweet 0
  • Privacy Policy
  • Contact

© 2023 LBNN - All rights reserved.

No Result
View All Result
  • Home
  • Business
  • Politics
  • Markets
  • Crypto
  • Economics
    • Manufacturing
    • Real Estate
    • Infrastructure
  • Finance
  • Energy
  • Creator Economy
  • Wealth Management
  • Taxes
  • Telecoms
  • Military & Defense
  • Careers
  • Technology
  • Artificial Intelligence
  • Investigative journalism
  • Art & Culture
  • Documentaries
  • Quizzes
    • Enneagram quiz
  • Newsletters
    • LBNN Newsletter
    • Divergent Capitalist

© 2023 LBNN - All rights reserved.