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Bitslice Vectors: A Software Approach to Customizable Data Precision on Processors with SIMD Extensions

机译:Bitslice向量:具有SIMD扩展的处理器上可自定义数据精度的软件方法

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Customizing the precision of data can provide attractive trade-offs between accuracy and hardware resources. Custom hardware and FPGA designs allow bit-level control over precision, but software is typically limited by the range of types supported by the underlying processor. We propose a new form of vector computing aimed at arrays of custom-precision data on general-purpose processors with SIMD extensions. We represent these vectors in bitslice format and use bitwise instructions to build arithmetic operators that operate on the customized bit precision. We construct a domain-specific code generator that builds bit-level customizable floating-point and integer operators for our vector types. Using a hardware circuit optimization tool we optimize our logical expressions, and synthesize fast software arithmetic operators for bitslice vector types. We evaluate the resulting code and find that advanced logic optimization significantly improves performance. Experiments on a platform with Intel AVX2 SIMD extensions show that this approach is efficient for vectors of low-precision custom floating-point types, while providing arbitrary bit precision.
机译:自定义数据的精度可以在精度和硬件资源之间提供有吸引力的权衡。自定义硬件和FPGA设计允许对精度进行比特级控制,但软件通常受底层处理器支持的类型范围限制。我们提出了一种新的载体计算形式,针对具有SIMD扩展的通用处理器的定制精度数据阵列。我们以bitslice格式表示这些向量,并使用按位指令构建在定制位精度上运行的算术运算符。我们构建一个特定于域的代码生成器,为向量类型构建位级可自定义的浮点和整数运算符。使用硬件电路优化工具,我们优化我们的逻辑表达式,并为Bitslice向量类型合成快速软件算术运算符。我们评估结果代码,并发现高级逻辑优化显着提高了性能。 Intel AVX2 SIMD扩展平台上的实验表明,这种方法对于低精度定制浮点类型的载体是有效的,同时提供任意比特精度。

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