Multi scale thinking for Semiconductors: Part3 - Need for Computing in Memory

Why is AI pushing our current computer hardware to its absolute absolute limits? In Part 3 of "Multi-Scale Thinking for Semiconductors," we transition from abstract thermodynamics into the most pressing hardware challenge of the AI era: The Memory Wall. We explore how the traditional separation of processing and memory (the Von Neumann architecture) creates an unsustainable energy bottleneck, where moving data between chips costs 1,000x more energy than the actual calculation. We introduce Compute-in-Memory (CIM) as the architectural breakthrough needed to close this gap and discuss why solving the AI power crisis requires an integrated, multi-scale modeling approach—from atomic material leakage up to circuit-level execution. Watch to find out why the future of AI isn't about faster processors, but about smarter memory. #SemiconductorResearch #IITGN #ComputeInMemory #AIChips #VonNeumann #DevicePhysics #ProjectSamarth