In‑Memory Computing: The AI Bottleneck No One Sees

AI isn’t slowing down because of compute — it’s slowing down because of data movement.
Every neural network wastes energy shuttling information between memory and processors. This is the Von Neumann bottleneck, and it’s becoming the biggest limit on AI scaling.

In this deep‑dive, we explore In‑Memory Computing — a breakthrough architecture that performs computation inside the memory array itself. No shuttling. No bottleneck. No wasted energy.

This is the hardware shift almost no one is talking about.

🔹 What You’ll Learn
Why modern AI is bottlenecked by memory, not compute

How In‑Memory Computing flips the architecture

The physics behind analog and digital compute‑in‑memory

Why this technology could deliver 10x–100x efficiency gains

How it enables real‑time AI in edge devices, robotics, wearables, and autonomous systems

The innovators building this future: Mythic, Rain Neuromorphics, MemryX, and more

How this fits into the broader shift toward neuromorphic, photonic, and edge‑native intelligence

🔹 Why This Matters
GPUs can’t scale forever.
Data centres are hitting power limits.
Edge devices need real‑time intelligence.
And AI workloads are exploding.

In‑Memory Computing is one of the most important — and most overlooked — breakthroughs in the future of AI hardware.

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