Keynote Talks

Abstract

As foundation models continue to scale, the dominant bottleneck is shifting from compute to memory—capacity, bandwidth, and data movement. This trend is especially acute for emerging agentic AI systems, where long contexts, persistent state, and multi-step reasoning place unprecedented pressure on the memory hierarchy. This talk argues that breaking the memory wall requires a coordinated effort across the entire stack, and offers a unified perspective spanning algorithms, systems, architecture, and silicon.

The talk first considers how memory demand can be reduced at its source, through algorithm- and system-level techniques that reshape how models store and move data during inference. It then turns to memory-centric accelerator design, showing how such techniques can be co-designed with the underlying hardware rather than treated in isolation. Finally, it looks toward silicon realization and the future of memory-centric accelerators purpose-built for foundation models and agent systems. The unifying thread—from memory compression to scheduling to memory-centric architecture and specialized chips—points toward a practical path for sustaining the continued scaling of foundation models.

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