RRAM tagged posts

Newly discovered role of phase separation can help develop memory devices for energy-efficient AI computing. Phase separation, when molecules part like oil and water, works alongside oxygen diffusion to help memristors — electrical components that store information using electrical resistance — retain information even after the power is shut off, according to a University of Michigan led study recently published in Matter.

Up to this point, explanations have not fully grasped how memristors retain information without a power source, known as nonvolatile memory, because models and experiments do not match up.

“While experiments have shown devices can retain information for over 10 years, the models used in the community show that information can only be retained for a few hours,”...

Transactions between processors and memory can consume 95% of the energy needed to do machine learning and AI, which severely limits battery life. A team of engineers has designed a system that can run AI tasks faster, and with less energy, by harnessing eight hybrid chips, each with its own data processor built right next to its own memory storage.

Smartwatches and other battery-powered electronics would be even smarter if they could run AI algorithms...

The more objects we make “smart,” from watches to entire buildings, the greater the need for these devices to store and retrieve massive amounts of data quickly without consuming too much power. Millions of new memory cells could be part of a computer chip and provide that speed and energy savings, thanks to the discovery of a previously unobserved functionality in a material called molybdenum ditelluride.

The 2D material stacks into multiple layers to build a memory cell...

Stanford engineers are developing new memory chips, based on materials other than silicon, to provide more energy-efficient ways to store data as digital zeros and ones. Credit: iStock / matejmo

Scientists often discover interesting things without completely understanding how they work. That has been the case with an experimental memory technology in which temperature and voltage work together to create the conditions for data storage. But precisely how was unknown. But when a Stanford team found a way to untangle the chip’s energy and heat requirements, their findings revealed a pleasant surprise: The process may be more energy efficient than was previously supposed. That’s good news for next-generation mobile devices whose batteries would last longer powering lower energy chips.

The new ...