UniCAIM introduces a groundbreaking hardware architecture that combines CAM/CIM technologies with novel KV cache pruning strategies to dramatically improve LLM inference efficiency for long text sequences.

• Achieves up to 2.0× speedup and 1.7× energy reduction over state-of-the-art accelerators
• Implements adaptive hybrid pruning that balances static and dynamic approaches
• Features innovative circuit-level optimizations for FeFET-based hardware
• Maintains model accuracy while significantly reducing memory and computational costs

This engineering breakthrough addresses a critical bottleneck in LLM deployment, enabling more efficient processing of long documents and conversations with reduced infrastructure requirements.

Original Paper: UniCAIM: A Unified CAM/CIM Architecture with Static-Dynamic KV Cache Pruning for Efficient Long-Context LLM Inference