在CoRAM抽象中实现互连和内存的自动定制(仅抽象)

Eric S. Chung, Michael Papamichael
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引用次数: 0

摘要

当开发在FPGA上运行的应用程序时,我们倾向于花费大量精力来制作自定义硬件加速数据路径,但盲目地转向FPGA供应商工具/库,为片上互连和外部接口提供默认解决方案。这通常会导致无效的通信或内存绑定实现,因为默认通用解决方案的设计和调优必然会在设计上做出妥协,以实现通用性。这是适得其反的,因为FPGA灵活的可重构性应该为我们提供巨大的机会,通过广泛的特定于应用的互连和接口ip的定制来提高性能和降低成本。这项工作提出了一个编译器,它可以生成自定义互连拓扑和连接,并具有适当缩放的容量,以最小的成本支持应用程序的精确通信需求。更具体地说,编译器分析为CoRAM抽象[1,2]开发的应用程序,分析其在硬件处理内核和外部DRAM组之间的连接性和带宽需求。其结果是一个非常微调的自定义拓扑软逻辑片上网络互连,这是由CONNECT NoC框架[3]实现的。我们对两个应用程序进行了广泛的评估,对标准CoRAM实现流进行基准测试,该实现流依赖于固定的通用调优的通用软逻辑片上网络。rtl驱动的评估显示,在不影响应用性能的情况下,有很大的机会减少面积和提高效率(提高48%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards automatic customization of interconnect and memory in the CoRAM abstraction (abstract only)
When developing applications to run on FPGAs, we tend to expend great effort on crafting the custom hardware acceleration datapath---but blindly turn to the FPGA vendor tool/library to provide default solutions for on-chip interconnect and external interfaces. This often leads to ineffective communication- or memory-bound implementations since the design and tuning of the default general-purpose solutions necessarily makes design compromises for generality. This is counterproductive as the FPGA's flexible reconfigurability should afford us great opportunities for performance gain and cost reduction through extensive application-specific customization of the interconnect and interface IPs. This work presents a compiler that generates custom interconnect topology and connectivity with appropriately scaled capacity to support an application's exact communication requirements at a minimized cost. More specifically, the compiler analyzes an application developed for the CoRAM abstraction [1,2] for its connectivity and bandwidth requirements between the hardware processing kernels and external DRAM banks. The result is an extremely fine-tuned custom-topology soft-logic network-on-chip interconnect, which is enabled by the CONNECT NoC framework [3]. We perform an extensive evaluation that benchmarks two applications against the standard CoRAM implementation flow that relies on a fixed generically-tuned general-purpose soft-logic network-on-chip. Our RTL-driven evaluation shows a large opportunity for area reduction and improved efficiency (up by 48%) without any impact on application performance.
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