论文标题

单簧管:基于RISC-V的基于算术算术经验主义的框架

CLARINET: A RISC-V Based Framework for Posit Arithmetic Empiricism

论文作者

Sharma, Niraj, Jain, Riya, Mohan, Madhumita, Patkar, Sachin, Leupers, Rainer, Rishiyur, Nikhil, Merchant, Farhad

论文摘要

许多工程和科学应用都需要高精度算术。 IEEE〜754-2008符合(浮点)算术是执行这些计算的事实上的标准。最近,已提出算术算术作为浮点算术的倒入替代品。与浮点数格式和算术相比,POSIT \ texttrademark数据表示和算术声明的几个绝对优势,包括更高的动态范围,更好的准确性和出色的性能区域折衷。但是,没有任何可访问的整体框架可以促进这些主张算术主张的验证,尤其是当主张涉及长期积累(quire)时。 在本文中,我们提出了一个合并的基于通用处理器的框架,以支持提议算术经验主义。该框架的最终用户具有使用位置和浮点算术算术无缝实验的自由,因为该框架是为两个数字系统共存的。 Melodica是一个质量算术核心,它实现了涉及Quire数据类型的参数融合操作。单簧管是基于RISC-V ISA的旋律的处理器。据我们所知,这是Quire与RISC-V核心的首次集成。为了显示单簧管平台的有效性,我们进行了广泛的申请研究,并基准了一些常见的线性代数和计算机视觉内核。我们在Xilinx FPGA上模拟单簧管,并提供利用和计时数据。单簧管和Melodica仍在积极开发中,可在开源的算术算术经验主义中使用。

Many engineering and scientific applications require high precision arithmetic. IEEE~754-2008 compliant (floating-point) arithmetic is the de facto standard for performing these computations. Recently, posit arithmetic has been proposed as a drop-in replacement for floating-point arithmetic. The posit\texttrademark data representation and arithmetic claim several absolute advantages over the floating-point format and arithmetic, including higher dynamic range, better accuracy, and superior performance-area trade-offs. However, there does not exist any accessible, holistic framework that facilitates the validation of these claims of posit arithmetic, especially when the claims involve long accumulations (quire). In this paper, we present a consolidated general-purpose processor-based framework to support posit arithmetic empiricism. The end-users of the framework have the liberty to seamlessly experiment with their applications using posit and floating-point arithmetic since the framework is designed for the two number systems to coexist. Melodica is a posit arithmetic core that implements parametric fused operations that uniquely involve the quire data type. Clarinet is a Melodica-enabled processor based on the RISC-V ISA. To the best of our knowledge, this is the first-ever integration of quire with a RISC-V core. To show the effectiveness of the Clarinet platform, we perform an extensive application study and benchmark some of the common linear algebra and computer vision kernels. We emulate Clarinet on a Xilinx FPGA and present utilization and timing data. Clarinet and Melodica remain actively under development and is available in open-source for posit arithmetic empiricism.

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