Erik Vermij, Leandro Fiorin, et al.
ACM TACO
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× to 20× energy efficiency improvement compared to alternative solutions.
Erik Vermij, Leandro Fiorin, et al.
ACM TACO
Leandro Fiorin, Erik Vermij, et al.
Int. J. Parallel Program
Giovanni Mariani, Andreea Anghel, et al.
CF 2015
Leandro Fiorin, Erik Vermij, et al.
CF 2015