RSVP II:下一代汽车矢量处理器

S. Chiricescu, S. Chai, K. Moat, B. Lucas, P. May, J. Norm, R. Essick, M. Schuette
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引用次数: 11

摘要

大量持续监测环境的传感器(如视频、雷达、激光、超声波等)正在普通汽车中找到它们的方式。处理这些传感器捕获的数据的算法本质上是流的,需要很高的计算速度。由于汽车环境的特点,这种计算必须在非常低的能源和成本预算下完成。可重构流矢量处理(RSVP/spl trade/)架构是一种加速流数据处理的矢量协处理器架构。本文介绍了RSVP体系结构及其第二种实现RSVP II。我们的结果显示运行编译代码的数据流函数有显著的加速。在车道跟踪应用程序上,RSVP II显示了令人印象深刻的性能结果。从性能/$和性能/mW的角度来看,RSVP架构与领先的DSP架构相比具有优势。由于易于编程,消除了手工调整的汇编代码,以及通过跨多个实现的二进制兼容性支持软件重用,因此大大缩短了上市时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
RSVP II: a next generation automotive vector processor
A large number of sensors (i.e., video, radar, laser, ultrasound, etc.) that continuously monitor the environment are finding their way in the average automobile. The algorithms processing the data captured by these sensors are streaming in nature and require a high rate of computation. Due to the characteristics of the automotive environment, this computation has to be delivered under very low energy and cost budgets. The reconfigurable streaming vector processing (RSVP/spl trade/) architecture is a vector coprocessor architecture which accelerates streaming data processing. This paper presents the RSVP architecture and its second implementation, RSVP II. Our results show significant speedups on data streaming functions running compiled code. On a lane tracking application, RSVP II shows impressive performance results. From a performance/$ and performance/mW perspective, RSVP architecture compares favorably with leading DSP architectures. The time to market is substantially reduced due to ease of programmability, elimination of hand-tuned assembly code, and support for software re-use through binary compatibility across multiple implementations.
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