This research introduces an innovative hardware solution to address memory bottlenecks in LLM inference by enhancing on-chip memory controllers in AI accelerators.

Key innovations:

• Specialized memory controller design that integrates compression techniques
• Reduces memory bandwidth and capacity demands for LLM inference
• Maintains inference quality while improving efficiency
• Hardware-level solution that complements existing optimization techniques (pruning, quantization)

This research offers significant engineering value by addressing a fundamental hardware constraint in AI deployment. By optimizing memory access patterns, the approach enables more efficient operation of large language models on existing hardware platforms.

Reimagining Memory Access for LLM Inference: Compression-Aware Memory Controller Design