MxGLUT introduces a reconfigurable LUT-centric broadcast dataflow accelerator with mixed-precision LUT-based PEs that unifies FP8-INT4 and FP8-FP8 GEMM without separate FP datapaths, reporting up to 2.16x prefill speedup and 0.492 TFLOPS/mm² area efficiency in 28nm synthesis.
ZeroQuant-FP: A Leap Forward in LLMs Post-Training W4A8 Quantization Using Floating-Point Formats
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P3-LLM delivers 4.9x average speedup over HBM-PIM for edge LLM inference by pairing hybrid-format quantization with iso-area-optimized low-precision PIM compute units and operator fusion.
The paper surveys techniques to speed up and reduce the resource needs of LLM inference, organized by data-level, model-level, and system-level changes, with comparative experiments on representative methods.
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MxGLUT: A Reconfigurable LUT-Centric Broadcast Dataflow Accelerator for Mixed-Precision GEMM
MxGLUT introduces a reconfigurable LUT-centric broadcast dataflow accelerator with mixed-precision LUT-based PEs that unifies FP8-INT4 and FP8-FP8 GEMM without separate FP datapaths, reporting up to 2.16x prefill speedup and 0.492 TFLOPS/mm² area efficiency in 28nm synthesis.
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P3-LLM: An Integrated NPU-PIM Accelerator for Edge LLM Inference Using Hybrid Numerical Formats
P3-LLM delivers 4.9x average speedup over HBM-PIM for edge LLM inference by pairing hybrid-format quantization with iso-area-optimized low-precision PIM compute units and operator fusion.
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A Survey on Efficient Inference for Large Language Models
The paper surveys techniques to speed up and reduce the resource needs of LLM inference, organized by data-level, model-level, and system-level changes, with comparative experiments on representative methods.