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arXiv 2007.01530cs.AR
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FPnew:一种用于能量比例传输计算的开源多格式浮点单元结构

FPnew: An Open-Source Multi-Format Floating-Point Unit Architecture for Energy-Proportional Transprecision Computing

Stefan Mach, Fabian Schuiki, Florian Zaruba, Luca Benini

摘要:

摩尔定律和功率墙的放缓需要向精细可调精度(a.k.a.)转变。transprecision)计算以减少能量足迹。因此,我们需要能够以高能量比例在宽范围的精度上执行浮点运算的电路。我们介绍了FPnew,一个高度可配置的开源transprecision浮点单元(TP-FPU),能够支持各种标准和自定义FP格式。为了证明FPnew在通用处理器架构中的灵活性和效率,我们扩展了RISC-V ISA,包括半精度,bfloat 16和8位FP格式的操作,以及SIMD向量和多格式操作。集成到32位RISC-V内核中,我们的TP-FPU可以将混合精度应用的执行速度提高1.67倍w.r.t.。FP 32基准,同时保持端到端精度并将系统能耗降低37%。我们还将FPnew集成到64位RISC-V内核中,支持标量或2、4或8路SIMD向量上的五种FP格式。对于该内核,我们在0.45V至1.2V的宽电压范围内测量了采用Globalfoundries 22 FDX技术制造的硅。该单元实现了178 Gflop/sW(在FP 64上)和2.95 Tflop/sW(在8位微型浮点数上)之间的前沿测量能效,以及3.2 Gflop/s和25.3 Gflop/s之间的性能。

英文摘要:

The slowdown of Moore's law and the power wall necessitates a shift towards finely tunable precision (a.k.a. transprecision) computing to reduce energy footprint. Hence, we need circuits capable of performing floating-point operations on a wide range of precisions with high energy-proportionality. We present FPnew, a highly configurable open-source transprecision floating-point unit (TP-FPU) capable of supporting a wide range of standard and custom FP formats. To demonstrate the flexibility and efficiency of FPnew in general-purpose processor architectures, we extend the RISC-V ISA with operations on half-precision, bfloat16, and an 8bit FP format, as well as SIMD vectors and multi-format operations. Integrated into a 32-bit RISC-V core, our TP-FPU can speed up execution of mixed-precision applications by 1.67x w.r.t. an FP32 baseline, while maintaining end-to-end precision and reducing system energy by 37%. We also integrate FPnew into a 64-bit RISC-V core, supporting five FP formats on scalars or 2, 4, or 8-way SIMD vectors. For this core, we measured the silicon manufactured in Globalfoundries 22FDX technology across a wide voltage range from 0.45V to 1.2V. The unit achieves leading-edge measured energy efficiencies between 178 Gflop/sW (on FP64) and 2.95 Tflop/sW (on 8-bit mini-floats), and a performance between 3.2 Gflop/s and 25.3 Gflop/s.

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。

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