在现代fpga上使用几种存储器的优化设计

Mehmet Gungor, Kai Huang, Stratis Ioannidis, M. Leeser
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引用次数: 0

摘要

针对数据中心的现代fpga旨在加速处理大数据问题。它们提供许多不同类型的存储器,包括片上存储器和板上存储器。最近增加的是高带宽内存(HBM),其优点已被其他人证明。然而,很少有研究关注不同内存类型之间的交互如何影响应用程序性能。我们研究了HBM和片上存储器(BRAM或URAM)的组合如何影响时钟速率和整体应用程序延迟。在这些设计中,片上存储器被用作存储在HBM中的大量数据的片上缓存。我们的实验表明,随着存储在BRAM或URAM中的数据大小的增加,可实现的时钟速度降低。这反过来可能导致性能下降。我们研究了乱码电路,具有高内存需求和乱序数据访问的安全功能评估(SFE)的实现,并研究了BRAM, URAM和HBM使用的不同选择如何改变其性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimizing Designs Using Several Types of Memories on Modern FPGAs
Modern FPGAs targeting data centers are designed to accelerate problems with large data. They offer many different types of memory including on-chip and on-board memories. A recent addition is High Bandwidth Memory (HBM), whose advantages have been demonstrated by others. However, there is little research that looks at how interactions among different memory types impact application performance. We investigate how a combination of HBM and on-chip memory (BRAM or URAM) impact clock rate and overall application latency. In these designs, the on-chip memory is used as an on-chip cache for the larger amounts of data stored in HBM. Our experiments show that as the size of data stored in BRAM or URAM increases, the achievable clock speed is reduced. This in turn may result in degraded performance. We examine Garbled Circuits, an implementation of Secure Function Evaluation (SFE) with high memory demands and out-of-order data access, and examine how different choices of BRAM, URAM and HBM usage alters its performance.
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