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The Memory that Breathes is an EE Times AI with Sally episode about 4DS Memory’s developing resistive RAM technology. Hosted by Sally Ward-Foxton and published on September 13, 2024, the company-sponsored interview features Peter Himes, 4DS Memory’s chief strategy officer. Himes describes ReRAM as a possible persistent memory tier alongside SRAM and DRAM—not a replacement for them—and discusses possible uses in AI systems. The performance benefits he describes are company claims, not independently demonstrated results in the episode.

What is ReRAM, actually?

Resistive random-access memory, or ReRAM, represents data through a cell’s electrical resistance: a programmed high- or low-resistance state can encode a bit. In the episode, Himes contrasts 4DS Memory’s approach with filamentary ReRAM, which he characterizes as forming and breaking a conductive filament. 4DS calls its method “Interface Switching ReRAM.”

According to 4DS Memory’s technology description, its material system is PCMO—praseodymium, calcium, manganese, and oxygen—and its account of switching involves oxygen-ion movement in a perovskite lattice. These are the company’s explanations of how its technology works, not an independent verification of the mechanism or its advantages.

How could ReRAM fit into AI memory?

Himes presents 4DS ReRAM as a potential persistent tier that could complement the volatile memories already used in computing. He discusses retaining relatively stable AI model data, including weights, as well as backup, recovery, and fault tolerance. In this proposed arrangement, ReRAM would add another place to keep data; it would not simply take the place of SRAM or DRAM.

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That distinction matters: the interview describes possible system roles, not deployed AI systems shown to use 4DS memory. Himes also says the technology is not a drop-in replacement and that memory architecture needs to be designed for its application. A partner would be needed to develop an application-specific memory cell or test chip.

Which performance benefits are established?

Himes attributes speed, endurance, and low energy per bit to 4DS’s area-based interface-switching mechanism. He also argues that persistent data could avoid the need for refresh while it is retained. The interview supplies no measured performance figures, independent test results, or comparison methodology for these claims, so it does not establish how 4DS ReRAM performs against other memory technologies.

The episode raises useful questions for evaluating any memory design—whether it is volatile, how it switches, and how speed, endurance, energy use, density, and system architecture compare. It does not provide evidence for ranking 4DS ReRAM against filamentary ReRAM, SRAM, or DRAM on those measures.

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How far along is 4DS with productizing the technology?

In the interview, Himes discusses 60nm cells and a goal of scaling to 20nm. On January 15, 2025, 4DS reported completing characterization and testing of its Fifth Platform Lot and said the Sixth Platform Lot would incorporate 20nm cells. That company-reported milestone and plan indicate development work; they do not establish that a commercial product has shipped.

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The company’s technology page describes its memory technology, but the reviewed company material does not identify a retail memory product. The episode’s statement that it is “not a drop-in replacement for anything that exists today” is Himes’s description of 4DS’s development stage in the 2024 interview, rather than an independently verified assessment of the current market.

What is the episode, and who is it for?

The Memory that Breathes is Episode 11 of EE Times’ AI with Sally. Sally Ward-Foxton hosts the conversation with Peter Himes, and the episode was sponsored by 4DS Memory. It is most useful to readers looking for an introduction to how one company describes its ReRAM approach and where it hopes the technology might fit in future systems. Because it is a sponsored interview, its explanations and benefits should be read as the company’s perspective rather than independent comparative validation.

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