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Memory-centric computing

Memory-centric AI computing is being pursued in several ways, including more bandwidth, memory-tiering software and processing near the data. SK hynix demonstrated tiered cache management and processing-in-memory in September. Neither demonstration shows that the memory bottleneck is solved.

low conviction concept

Research snapshot · updated Nov 12, 2026

Core thesis

Data movement consumes 90%+ of energy in AI inference. Processing-in-memory eliminates this by computing where data lives. Samsung HBM-PIM, UPMEM, and Mythic pursue different approaches. If it works at scale, it could be more energy-efficient than GPUs for inference. But the programming model is radically different from conventional computing, limiting software ecosystem development.

State of the art (2026)

In 2026 the field has split. The commercial winner is bandwidth scaling: SK hynix, Samsung and Micron all reached HBM4 mass production this year, feeding NVIDIA Vera Rubin, which entered full production in June, plus AMD MI400. CXL pooling and the UALink 200G interconnect are disaggregating the rack. True processing-in-memory remains the smaller story. The breakout is d-Matrix, whose SRAM-based Corsair inference accelerator entered full production in June 2026, pairing with GPUs to claim roughly 10x decode-phase speed-ups. Analog and ReRAM compute-in-memory (Mythic, EnCharge, IBM Research) stay at edge and research scale. The thesis holds at the level of memory economics, not at the level of computing inside DRAM.

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Signal stack

Evidence stacked leading → lagging

8 signals
talent
research
patent
expert
operational
market

Technology-native KPIs

Metrics that predict trajectory, tracked over time

3 tracked
Processing-in-memory chip revenue
Memory bandwidth wall impact
PIM research publications

Landscape map

Who builds what, and who depends on whom

108 players · 6 layers

Catalyst calendar

Dated events that will move the position

4 ahead

Technology roadmap

Milestones on the path to maturity

8 milestones

Watchlists

Companies, people and papers, each with a condition for removing it

20 · 20
Companies · 20
People · 20

Decision frameworks

What our view means for your job

Locked
Public Equity
PE / VC
Corporate Leader

Thesis changelog

When our view changed, and why

7 updates

Change our mind

2 disconfirming conditions

You've read the verdict. The file is much deeper.

The full signal stack, technology-native KPIs tracked over time, the landscape of who depends on whom, the dated catalyst calendar, decision frameworks for every desk, live watchlists and a record of every time our view on Memory-centric computing has changed. All of it is in CanaryIQ.