论文标题
DRMAP：用于卷积神经网络节能处理的通用DRAM数据映射策略
DRMap: A Generic DRAM Data Mapping Policy for Energy-Efficient Processing of Convolutional Neural Networks
论文作者
论文摘要
许多卷积神经网络（CNN）加速器面临性能和能源效率的挑战，这对于嵌入式实施至关重要，这是由于DRAM访问潜伏期和能量高。最近，已经提出了一些DRAM架构来利用子阵列级并行性，以减少访问延迟。为此，我们提出了一种设计空间探索方法，以研究不同DRAM体系结构的不同映射策略的潜伏和能量，并确定帕累托最佳的设计选择。结果表明，可以通过有序优先级的映射策略来实现节能的DRAM访问，以最大程度地提高行缓冲区命中，银行和子阵列级别的并行性。
Many convolutional neural network (CNN) accelerators face performance- and energy-efficiency challenges which are crucial for embedded implementations, due to high DRAM access latency and energy. Recently, some DRAM architectures have been proposed to exploit subarray-level parallelism for decreasing the access latency. Towards this, we present a design space exploration methodology to study the latency and energy of different mapping policies on different DRAM architectures, and identify the pareto-optimal design choices. The results show that the energy-efficient DRAM accesses can be achieved by a mapping policy that orderly prioritizes to maximize the row buffer hits, bank- and subarray-level parallelism.
