PLAM：一个正对数近似乘法器
PLAM: a Posit Logarithm-Approximate Multiplier
摘要：
Posit数字系统于2017年推出，作为浮点数的替代品。从那时起，社区已经探索了它在神经网络相关任务中的应用，并产生了一些单元设计，这些设计与浮点运算相比仍然远远没有竞争力。本文提出了一种Posit对数近似乘法（PLAM）方案，以显著降低深度神经网络架构中最耗电的单元--乘法器的复杂度。实验结果表明，与现有的硬件乘法器相比，该方法在不降低乘法精度的前提下，使硬件乘法器的面积、功耗和延迟分别降低了72.86%、81.79%和17.01%.
英文摘要：
The Posit Number System was introduced in 2017 as a replacement for floating-point numbers. Since then, the community has explored its application in Neural Network related tasks and produced some unit designs which are still far from being competitive with their floating-point counterparts. This paper proposes a Posit Logarithm-Approximate Multiplication (PLAM) scheme to significantly reduce the complexity of posit multipliers, the most power-hungry units within Deep Neural Network architectures. When comparing with state-of-the-art posit multipliers, experiments show that the proposed technique reduces the area, power, and delay of hardware multipliers up to 72.86%, 81.79%, and 17.01%, respectively, without accuracy degradation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
