Wafer-Scale AI Compute: A System Software Perspective
a month ago
- #Wafer-Scale Computing
- #AI Hardware
- #System Software
- AI models are pushing traditional computing architectures to their limits, leading to the development of wafer-scale AI chips.
- Wafer-scale AI chips integrate hundreds of thousands of cores and massive on-chip memory onto a single wafer for improved performance and efficiency.
- System software must evolve to fully utilize the capabilities of wafer-scale hardware.
- PLMR is a conceptual model capturing key architectural traits of wafer-scale systems: Massive Parallelism (P), Non-uniform Memory Access Latency (L), Constrained per-core Local Memory (M), and Constrained Routing Resources (R).
- Existing AI software stacks are not optimized for wafer-scale systems, leading to inefficiencies.
- WaferLLM is a system designed for wafer-scale inference, achieving sub-millisecond-per-token latency.
- Wafer-scale systems offer superior scaling efficiency compared to multi-chip designs, reducing communication bottlenecks.
- Future directions include rethinking AI model architectures, advancing wafer-scale software, and designing more efficient hardware.