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A power-aware code-compression design for RISC/VLIW architecture

机译:用于RISC / VLIW架构的功耗感知代码压缩设计

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We studied the architecture of embedded computing systems from the viewpoint of power consumption in memory systems and used a selective-code-compression (SCC) approach to realize our design. Based on the LZW (Lempel-Ziv-Welch) compression algorithm, we propose a novel cost effective compression and decompression method. The goal of our study was to develop a new SCC approach with an extended decision policy based on the prediction of power consumption. Our decompression method had to be easily implemented in hardware and to collaborate with the embedded processor. The hardware implementation of our decompression engine uses the TSMC 0.18 μm-2p6m model and its cell-based libraries. To calculate power consumption more accurately, we used a static analysis method to estimate the power overhead of the decompression engine. We also used variable sized branch blocks and considered several features of very long instruction word (VLIW) processors for our compression, including the instruction level parallelism (ILP) technique and the scheduling of instructions. Our code-compression methods are not limited to VLIW machines, and can be applied to other kinds of reduced instruction set computer (RISC) architecture.
机译:我们从内存系统中的功耗角度研究了嵌入式计算系统的体系结构,并使用选择性代码压缩(SCC)方法来实现我们的设计。基于LZW(Lempel-Ziv-Welch)压缩算法,我们提出了一种经济高效的压缩和解压缩方法。我们研究的目的是开发一种新的SCC方法,该方法具有基于功耗预测的扩展决策策略。我们的解压缩方法必须易于在硬件中实现,并与嵌入式处理器协作。我们的解压缩引擎的硬件实现使用TSMC 0.18μm-2p6m模型及其基于单元的库。为了更准确地计算功耗,我们使用静态分析方法来估算减压引擎的功耗。我们还使用了可变大小的分支块,并考虑了超长指令字(VLIW)处理器的几种功能进行压缩,包括指令级并行(ILP)技术和指令调度。我们的代码压缩方法不仅限于VLIW机器,还可以应用于其他类型的精简指令集计算机(RISC)体系结构。

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