A dissertation titled “Novel Non-Volatile Memory Devices and Applications” was submitted by a researcher at University of California Berkeley. “This dissertation focuses on novel non-volatile memory ...

Artificial Intelligence (AI) and Machine Learning (ML) applications are driving increased demand for high-performance, low-power memory solutions across consumer, medical, and industrial markets.

This FAQ will look at a lesser-known but commercially available RAM technology called resistive random-access memory (RRAM) ...

In the era of big data and machine learning technology, performing low-power in-memory computing operations is becoming an increasingly important requisite. While much advance has been achieved in ...

Scientists have achieved a breakthrough in the development of non-volatile phase change memory−−a type of electronic memory that can store data even when the power is turned off−−in a material that ...

A long-running problem in the computer world is that DRAM is the fastest memory available but also volatile, so it can't hold onto its data when power is shut off. This makes it useless for data ...

Researchers from Lancaster University have developed a new type of computer memory product that they say combines the best features of DRAM memory and SSD storage…. and then surpasses them. Like flash ...

UD’s Tingyi Gu receives NSF CAREER award to study materials that can create more reliable, less energy-intensive forms of computer memory To develop the types of high-speed, energy-efficient ...

A significant milestone has been reached in the advancement of non-volatile phase change memory, a form of electronic memory capable of retaining data even when power is disconnected, in a material ...

Forbes contributors publish independent expert analyses and insights. This is the third in a set of four blogs about projections for digital storage and memory for the following year that we have been ...

Compute-in-memory (CiM) has become an attractive computing paradigm to address the memory and power walls in traditional designs for deep learning applications. With CiM, part of the computation ...