Abstract
Abstract: Posit number system has been used in many applications, especially the deep learning. Because of how well its nonuniform number distribution aligns with deep learning's data distribution, deep learning's training process can be sped up. The hardware multiplier is typically built with the widest mantissa bit-width available due to the flexibility of posit numbers' bitwidth. Such multiplier designs consume a lot of power since the mantissa bit-width is not necessarily the maximum value. This is especially true when the mantissa bit-width is tiny. The mantissa multiplier is still built to have the widest bit-width feasible, but it is broken into numerous smaller multipliers. At run-time, just the necessary tiny multipliers are turned on. The regime bitwidth, which can be used to determine the mantissa bit-width, controls those smaller multipliers. This design technique is applied to 8-bit, 16-bit, and 32-bit posit formats.
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