The bottleneck slowing progress

The future of computing is running into a stubborn constraint.
Memory is fast but forgetful. Storage remembers, but it is too slow.

This gap between compute, memory, and storage, known as the Memory Wall, has been a challenge for decades. But as AI models grow ever larger, data centers expand to unprecedented scale, and edge devices demand instant-on performance, the gap has become a critical bottleneck. Systems spend more time moving data than doing meaningful work, wasting energy and slowing progress.

The consequences are not theoretical. AI data centers are consuming ever more power, straining infrastructure and budgets alike. And the underlying inefficiency is not only a performance issue, it is a sustainability challenge that the industry can no longer ignore.

The Strategic opening for memory innovation

The search for scalable, energy-efficient solutions is now a top priority for both industry and governments. Memory, often overlooked compared to processors, has become a central focus because it represents both the biggest inefficiency in modern computing and one of the clearest opportunities for disruption.

Regions like Europe, which consume a large share of the world’s semiconductor memory but produce
virtually none, see this as more than a technical challenge. It is a matter of digital sovereignty, economic competitiveness, and climate responsibility. Whoever develops scalable, power-efficient memory first will not only ease the Memory Wall but also gain strategic advantage in the race to build AI infrastructure.

Memory built for performance and persistence

Closing this gap requires memory that is both fast and persistent, combining the speed of DRAM with the ability to retain data like storage. This combination solves one of the oldest trade-offs in computing: the choice between high-speed, volatile memory and slower, persistent storage.

New materials and architectures are making this possible at scale. By leveraging hafnium oxide, a material already used widely in semiconductors, these next-generation memories can be manufactured on today’s production lines with minimal adjustments. That means they can scale quickly and cost-effectively, a critical factor for industries and investors seeking real impact rather than theoretical breakthroughs.

Two products redefining the memory landscape

The first wave of these innovations is arriving through two complementary technologies: DRAM+ and CACHE+.

— DRAM+ enhances traditional DRAM by adding non-volatility. Systems can now retain information even when powered down, eliminating energy-intensive reloads and downtime. This is a game-changer for AI clusters, hyperscale data centers, edge networks, automotive, and industrial systems, where constant uptime and efficiency are vital.

— CACHE+ reimagines SRAM, the high-speed CACHE used throughout computing. It delivers ten times the density of conventional SRAM while reducing standby power by a factor of ten. It also adds persistence, and is designed to integrate seamlessly with next-generation chiplet architectures for ultra-high-performance AI inference systems. Together, these capabilities unlock new possibilities for system designers who are rethinking architectures from the ground up.

Both products target what industry leaders like Nvidia’s Jensen Huang and former Google CEO Eric Schmidt have warned about: AI data centers cannot continue consuming power at their current trajectory. Solutions that deliver speed without efficiency gains will not be enough.

Designed to scale from day one

A major challenge for many experimental memory technologies is the jump from lab to fab. These new solutions are designed specifically to scale from day one. By using materials and processes already proven in high-volume manufacturing, they avoid the multi-year delays and high costs associated with entirely new fabrication technologies.

For investors, system builders, and governments, this matters. It means these memory solutions can move from pilot programs to mass production quickly, making them one of the few near-term answers to the performance and energy crises facing AI infrastructure.

Beyond components: System-level transformation

Solving the Memory Wall is not just about the memory chips themselves. To deliver maximum impact, these innovations are paired with hardware-accelerated cards, appliances, and reference architectures optimized for AI training and inference workloads.

This system-level approach ensures that organizations can integrate the technology quickly, benefit from its capabilities immediately, and reduce the complexity of redesigning infrastructure. It is not just about faster components, but about creating a new baseline for how systems scale, perform, and consume energy.

The path beyond the Memory Wall

The Memory Wall has constrained the growth of computing for decades. With AI, cloud, and edge systems now scaling at unprecedented rates, overcoming it is no longer optional. The industry needs fast, persistent, energy-efficient memory built for scalable manufacturing, paired with system-level innovation that unlocks its full potential.

With solutions like DRAM+ and CACHE+, the next generation of computing can move past this bottleneck. Data centers, AI factories, and intelligent devices can become not just faster, but more reliable, sustainable, and ready for the demands of the digital economy ahead.