You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Customizing the precision of data can provide attractive trade-offs between accuracy and hardware resources. We propose a novel form of vector computing aimed at arrays of custom-precision floating point data. We represent these vectors in bitslice format. Bitwise instructions are used to implement arithmetic circuits in software that operate on customized bit-precision. Experiments show that this approach can be efficient for vectors of low-precision custom floating point types, while providing arbitrary bit precision.

Cited Abstract(s):
[1]: Although SIMD (Single Instruction stream Multiple Data stream) parallel computers have existed for decades, it is only in the past few years that a new version of SIMD has evolved: SIMD Within A Register (SWAR). Unlike other styles of SIMD hardware, SWAR models are tuned to be integrated within conventional microprocessors, using their existing memory reference and instruction handling mechanisms, with the primary goal of improving the speed of specific multimedia operations. Because the SWAR implementations for various microprocessors vary widely and each is missing instructions for some SWAR operations that are needed to support a more general, portable, high-level SIMD execution model, this paper focuses on how these missing operations can be implemented using either the existing SWAR hardware or even conventional 32-bit integer instructions. In addition, SWAR offers a few new challenges for compiler optimization, and these are briefly introduced.
SWAR [1] , SIMD Within A Register, is the most closely related work to our bitslice floating point vector computation. SWAR uses logic operations to implement integer operations. Partitioned operations are the main focus of most of the hardware support for SWAR. Instead of keeping the field size as the size of desirable data types, we use one single bit to hold one bit of a data element so that the number of elements processed in parallel is not decided by the width of data type but the max width of registers (including SIMD registers). Meanwhile, we use logic operations to implement the actual hardware logic for a single bit of the data element.