Multiplication is indispensable and is one of the core operations in many modern applications including signal processing and neural networks. Conventional right-to-left (RL) multiplier extensively contributes to the power consumption, area utilization and critical path delay in such applications. This paper proposes a low latency multiplier based on online or left-to-right (LR) arithmetic which can increase throughput and reduce latency by digit-level pipelining. Online arithmetic enables overlapping successive operations regardless of data dependency because of the most significant digit first mode of operation. To produce most significant digit first, it uses redundant number system and we can have a carry-free addition, therefore, the delay of the arithmetic operation is independent of operand bit width. The operations are performed digit by digit serially from left to right which allows gradual increase in the slice activities making it suitable for implementation on reconfigurable devices. Serial nature of the online algorithm and gradual increment/decrement of active slices minimize the interconnects and signal activities resulting in overall reduction of area and power consumption. We present online multipliers with; both inputs in serial, and one in serial and one in parallel. Pipelined and non-pipelined designs of the proposed multipliers have been synthesized with GSCL 45nm technology on Synopsys Design Compiler. Thorough comparative analysis has been performed using widely used performance metrics. The results show that the proposed online multipliers outperform the RL multipliers.

翻译：乘法是现代许多应用（包括信号处理和神经网络）中不可或缺的核心运算之一。传统的从右到左（RL）乘法器在这些应用中显著贡献了功耗、面积利用和关键路径延迟。本文提出了一种基于在线算术或从左到右（LR）算术的低延迟乘法器，该乘法器可通过数字级流水线提高吞吐量并减少延迟。在线算术因采用最高有效位优先操作模式，能够在不考虑数据依赖性的前提下重叠连续操作。为了生成最高有效位优先的输出，它使用冗余数系并实现无进位加法，从而使算术运算的延迟与操作数位宽无关。操作从左侧以逐位串行方式执行，允许片内活动逐渐增加，使其适用于可重构器件实现。在线算法的串行特性及活动片的逐步增减最小化了互连和信号活动，从而整体降低面积和功耗。我们提出了两种在线乘法器：一种两个输入均为串行，另一种一个输入串行一个输入并行。所提出的乘法器的流水线与非流水线设计已在GSCL 45nm工艺技术下通过Synopsys Design Compiler进行综合。使用广泛采用的性能指标进行了全面的对比分析。结果表明，所提出的在线乘法器性能优于RL乘法器。