基于赛灵思fpga的基于RISC-V的mpsoc模块化存储系统

Ahmed Kamaleldin, Muhammad Ali, P. Rad, Marcus Gottschalk, D. Göhringer
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引用次数: 9

摘要

当前的应用领域,如移动机器人或物联网,需要低能耗的高计算能力。因此,mpsoc作为高性能嵌入式计算的合适平台被广泛应用。最近,RISC-V指令集架构的出现促使SoC设计者在mpsoc设计中采用它作为一种低成本的模块化处理器,适合在不同的硬件平台上实现。此外,这些特性使RISC-V成为FPGA软核处理器的有趣候选。在本文中,我们提出了一种基于轻量级RISC-V的MPSoC架构的模块化混合存储系统。混合存储器的实现包括用于指令和数据的片上全局刮擦板,用于处理元件之间的通信和同步。除了与每个处理元素相关联的紧耦合内存之外,还可以为私有计算提供低延迟内存访问。此外,在pe数量、共享/私有内存大小和内存映射外设数量方面,完整的MPSoC架构是可扩展和可配置的。开发了一个基准测试环境,以评估所提出的混合存储系统在内存访问延迟和内存带宽方面的性能及其对计算时间的影响。完整的MPSoC架构在赛灵思Zynq 7000 FPGA器件上实现和测试。
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Modular Memory System for RISC-V Based MPSoCs on Xilinx FPGAs
Current application domains, like mobile robotics, or internet of things require high computational power associated with low energy consumption. Therefore, MPSoCs are widely used as an adequate platform for high performance embedded computation. Recently, the emergence of RISC-V instruction set architecture drives SoC designers to adopt it in the design of MPSoCs as a cost-free, modular processor and suitable to be implemented in different hardware platforms. Furthermore, these characteristics make the RISC-V an interesting candidate for an FPGA soft-core processor. In this paper, we present a modular hybrid memory system for a lightweight RISC-V based MPSoC architecture. The implementation of the hybrid memory consists of a global scratchpad on-chip shared memory for both instruction and data for the purpose of communication and synchronization between the processing elements. In addition to a tightly coupled memory associated with each processing element for low latency memory access for private computation. Moreover, the complete MPSoC architecture is scalable and configurable, in terms of the number of PEs, shared/private memory sizes and the number of memory mapped peripherals. A benchmarking environment is developed to evaluate the performance of the proposed hybrid memory system in terms of memory access latency and memory bandwidth and their impact on the computation time. The complete MPSoC architecture is implemented and tested on a Xilinx Zynq 7000 FPGA device.
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