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This work introduces fractional power-of-two quantization to reduce multiply-accumulate costs in LLM inference by enabling multiplier-free operations with shift-add logic. It addresses accuracy degradation issues found in traditional power-of-two quantization while maintaining efficiency.
This work introduces fractional power-of-two quantization to reduce multiply-accumulate costs in LLM inference by enabling multiplier-free operations with shift-add logic. It addresses accuracy degradation issues found in traditional power-of-two quantization while maintaining efficiency.
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Large Language Models (LLMs) demand efficient inference, where the multiply-accumulate (MAC) operations in linear projections dominate compute and energy cost. Post-training quantization (PTQ) reduces this cost by mapping weights and activations to low-bit integers. Power-of-Two (PoT) weight quantization promises...
Efficient LLM inference is critical due to the high compute and energy demands of multiply-accumulate operations. This method offers a promising approach to reduce these costs without sacrificing model accuracy.
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