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A tightly-coupled hardware controller to improve scalability and programmability of shared-memory heterogeneous clusters

机译:紧密耦合的硬件控制器,可提高共享内存异构集群的可伸缩性和可编程性

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Modern designs for embedded many-core systems increasingly include application-specific units to accelerate key computational kernels with orders-of-magnitude higher execution speed and energy efficiency compared to software counterparts. A promising architectural template is based on heterogeneous clusters, where simple RISC cores and specialized HW units (HWPU) communicate in a tightly-coupled manner via L1 shared memory. Efficiently integrating processors and a high number of HW Processing Units (HWPUs) in such an system poses two main challenges, namely, architectural scalability and programmability. In this paper we describe an optimized Data Pump (DP) which connects several accelerators to a restricted set of communication ports, and acts as a virtualization layer for programming, exposing FIFO queues to offload “HW tasks” to them through a set of lightweight APIs. In this work, we aim at optimizing both these mechanisms, for respectively reducing modules area and making programming sequence easier and lighter.
机译:嵌入式多核系统的现代设计越来越多地包含特定于应用程序的单元,以加速关键计算内核,与软件同类产品相比,其执行速度和能源效率提高了几个数量级。一个有前途的体系结构模板基于异构集群,其中简单的RISC内核和专用硬件单元(HWPU)通过L1共享内存以紧密耦合的方式进行通信。在这样的系统中高效集成处理器和大量硬件处理单元(HWPU)带来了两个主要挑战,即体系结构可扩展性和可编程性。在本文中,我们描述了一种优化的数据泵(DP),该数据泵将多个加速器连接到一组受限的通信端口,并充当编程的虚拟化层,公开FIFO队列,以通过一组轻量级API向其卸载“硬件任务” 。在这项工作中,我们旨在优化这两种机制,以分别减少模块面积并简化编程顺序并减轻其负担。

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