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FPGA-Based Near-Memory Acceleration of Modern Data-Intensive Applications
IEEE Micro ( IF 3.6 ) Pub Date : 2021-06-10 , DOI: 10.1109/mm.2021.3088396
Gagandeep Singh 1 , Mohammed Alser 1 , Damla Senol Cali 2 , Dionysios Diamantopoulos 3 , Juan Gomez-Luna 1 , Henk Corporaal 4 , Onur Mutlu 1
Affiliation  

Modern data-intensive applications demand high computational capabilities with strict power constraints. Unfortunately, such applications suffer from a significant waste of both execution cycles and energy in current computing systems due to the costly data movement between the computation units and the memory units. Genome analysis and weather prediction are two examples of such applications. Recent field-programmable gate arrays (FPGAs) couple a reconfigurable fabric with high-bandwidth memory (HBM) to enable more efficient data movement and improve overall performance and energy efficiency. This trend is an example of a paradigm shift to near-memory computing. We leverage such an FPGA with HBM for improving the prealignment filtering step of genome analysis and representative kernels from a weather prediction model. Our evaluation demonstrates large speedups and energy savings over a high-end IBM POWER9 system and a conventional FPGA board with DDR4 memory. We conclude that FPGA-based near-memory computing has the potential to alleviate the data movement bottleneck for modern data-intensive applications.

中文翻译:

现代数据密集型应用的基于 FPGA 的近内存加速

现代数据密集型应用程序需要具有严格功率限制的高计算能力。不幸的是,由于计算单元和存储器单元之间的数据移动成本高昂,这些应用程序在当前计算系统中遭受了执行周期和能量的显着浪费。基因组分析和天气预报是此类应用的两个示例。最近的现场可编程门阵列 (FPGA) 将可重构结构与高带宽存储器 (HBM) 结合在一起,以实现更高效的数据移动并提高整体性能和能效。这种趋势是向近内存计算的范式转变的一个例子。我们利用这种带有 HBM 的 FPGA 来改进基因组分析的预比对过滤步骤和来自天气预报模型的代表性内核。我们的评估表明,与高端 IBM POWER9 系统和具有 DDR4 内存的传统 FPGA 板相比,可以实现大幅加速和节能。我们得出的结论是,基于 FPGA 的近内存计算有可能缓解现代数据密集型应用程序的数据移动瓶颈。
更新日期:2021-07-06
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