MMEE encodes dataflow decisions in matrix form for fast exhaustive search, delivering 40-69% lower latency and energy use than prior methods while running 64-343x faster.
Dynamic sparse attention for scalable transformer acceleration
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D-Legion proposes a scalable architecture of Legions containing adaptive-precision systolic array cores that accelerates quantized LLM matrix multiplications, delivering up to 8.2x lower latency and 3.8x higher memory savings versus prior designs.
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Fast Cross-Operator Optimization of Attention Dataflow
MMEE encodes dataflow decisions in matrix form for fast exhaustive search, delivering 40-69% lower latency and energy use than prior methods while running 64-343x faster.
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D-Legion: A Scalable Many-Core Architecture for Accelerating Matrix Multiplication in Quantized LLMs
D-Legion proposes a scalable architecture of Legions containing adaptive-precision systolic array cores that accelerates quantized LLM matrix multiplications, delivering up to 8.2x lower latency and 3.8x higher memory savings versus prior designs.