Understand and Accelerate Memory Processing Pipeline for Disaggregated LLM Inference
Modern large language models (LLMs) increasingly depends on efficient long-context processing and generation mechanisms, including sparse attention, retrieval-augmented generation (RAG), and compressed contextual memory, to support complex reasoning. We show that these optimizations can be unified into a four-step memory processing pipeline: Prepare Memory, Compute Relevancy, Retrieval, and Apply to Inference. Through systematic profiling, we identify a 22%-97% memory processing overhead in LLM inference and strong heterogeneity in its computational characteristics. Motivated by this insight, we argue that heterogeneous systems are well-suited to accelerate memory processing and thus end-to-end inference. We demonstrate this approach on a GPU-FPGA system by offloading sparse, irregular, and memory-bounded operations to FPGAs while retaining compute-intensive operations on GPUs. Evaluated on an AMD MI210 GPU and an Alveo U55C FPGA, our system is 1.04sim2.2times faster and requires 1.11sim4.7times less energy across multiple LLM inference optimizations than the GPU baseline (similar results hold on NVIDIA A100). These results establish heterogeneous systems as a practical direction for efficient LLM memory processing and inform future heterogeneous hardware design.
69ce0006acf91530b21dfa60
Zifan He
claimed_verified
2026-04-02T08:13:06.182Z
6542bbc0bcc3731b0debc628
/avatars/7bcbff0738cf7efb9fbff73084446ea6.svg
Zifan He
OswaldHe123
user
OswaldHe123
69ce0006acf91530b21dfa61
Rui Ma
69ce0006acf91530b21dfa62
Yizhou Sun
69ce0006acf91530b21dfa63
Jason Cong
Understand and Accelerate Memory Processing Pipeline for Disaggregated LLM Inference
Modern large language models (LLMs) increasingly depends on efficient long-context processing and generation mechanisms, including sparse attention, retrieval-augmented generation (RAG), and compressed contextual memory, to support complex reasoning. We show that these optimizations can be unified into a four-step memory processing pipeline: Prepare Memory, Compute Relevancy, Retrieval, and Apply to Inference. Through systematic profiling, we identify a 22%-97% memory processing overhead in LLM inference and strong heterogeneity in its computational characteristics. Motivated by this insight, we argue that heterogeneous systems are well-suited to accelerate memory processing and thus end-to-end inference. We demonstrate this approach on a GPU-FPGA system by offloading sparse, irregular, and memory-bounded operations to FPGAs while retaining compute-intensive operations on GPUs. Evaluated on an AMD MI210 GPU and an Alveo U55C FPGA, our system is 1.04sim2.2times faster and requires 1.11sim4.7times less energy across multiple LLM inference optimizations than the GPU baseline (similar results hold on NVIDIA A100). These results establish heterogeneous systems as a practical direction for efficient LLM memory processing and inform future heterogeneous hardware design.
Heterogeneous GPU-FPGA systems can accelerate large language model inference by offloading memory-intensive operations to FPGAs, achieving significant performance and energy improvements.
large language models sparse attention retrieval-augmented generation compressed contextual memory memory processing pipeline heterogeneous systems GPU-FPGA system memory processing overhead computational characteristics heterogeneous hardware design