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A Survey on System-Level Design of Neural Network Accelerators
2021
Journal of Integrated Circuits and Systems
In this paper, we present a brief survey on the system-level optimizations used for convolutional neural network (CNN) inference accelerators. For the nested loop of convolutional (CONV) layers, we discuss the effects of loop optimizations such as loop interchange, tiling, unrolling and fusion on CNN accelerators. We also explain memory optimizations that are effective with the loop optimizations. In addition, we discuss streaming architectures and single computation engine architectures that
doi:10.29292/jics.v16i2.505
fatcat:ibbkeob42jepbguezlptws2qha