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Introducing a novel 2-bit quantization method, signed-zero ternary, enhances information density in models, suggesting improved performance.
Quantization is usually regarded as a means to trade quality of performance for reduced compute requirements, i.e., as a suboptimal approximation. However, if examined in terms of a fixed overall resource budget, a very different perspective arises. We introduce Signed-Zero Ternary (SZT), a 2-bit quantization that deterministically provides gradient information with no forward-path penalty. Our analysis provides evidence that it may improve information density compared to non-quantized alternatives.
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Jeffrey Uhlmann (2025) studied this question.
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