Near-Memory Acceleration for Radio Astronomy

Processing-in-memory and near-memory computing have recently been rediscovered as a way to alleviate the “memory wall problem” of traditional computing architectures. In this paper, we discuss the implementation of a 3D-stacked near-memory accelerator, targeting radio astronomy and scientific applications. After exploring the design space of the architecture by focusing on minimizing the execution power of the processing pipeline of the SKA1-Low central signal processor, we show that our accelerator can achieve an energy efficiency of up to 390 GFLOPS/W, corresponding to an energy consumption one order of magnitude lower than alternative state-of-the-art implementations. When running additional mathematical and streaming-oriented kernels, our accelerator achieves from 6.4<inline-formula><tex-math notation="LaTeX">$\times$</tex-math><alternatives> <inline-graphic xlink:href="fiorin-ieq1-2748580.gif"/></alternatives></inline-formula> to 20<inline-formula> <tex-math notation="LaTeX">$\times$</tex-math><alternatives><inline-graphic xlink:href="fiorin-ieq2-2748580.gif"/> </alternatives></inline-formula> energy efficiency improvement compared to alternative solutions.

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