论文标题
使用神经形态计算的浮点乘法
Floating-Point Multiplication Using Neuromorphic Computing
论文作者
论文摘要
神经形态计算描述了使用VLSI系统在模仿神经生物结构中的使用，也被视为传统von Neumann架构的有前途替代方案。任何新的计算体系结构都需要一个可以执行浮点算术的系统。在本文中，我们描述了执行IEEE 754符合浮点乘积的神经形态系统。复杂的乘法过程被分为由组件指数加法器，偏减，Mantissa乘法器和/uf的符号执行的较小子任务。我们研究每位神经元数量对准确性和位错误率的影响，并估算每个组件所需的最佳神经元数。
Neuromorphic computing describes the use of VLSI systems to mimic neuro-biological architectures and is also looked at as a promising alternative to the traditional von Neumann architecture. Any new computing architecture would need a system that can perform floating-point arithmetic. In this paper, we describe a neuromorphic system that performs IEEE 754-compliant floating-point multiplication. The complex process of multiplication is divided into smaller sub-tasks performed by components Exponent Adder, Bias Subtractor, Mantissa Multiplier and Sign OF/UF. We study the effect of the number of neurons per bit on accuracy and bit error rate, and estimate the optimal number of neurons needed for each component.
