论文标题
简单和高效率计算的内存体系结构中的向量
Vector In Memory Architecture for simple and high efficiency computing
论文作者
论文摘要
数据运动是当代系统架构的主要挑战之一。接近数据处理（NDP）通过将计算更接近内存来减轻此问题，从而避免了过多的数据移动。我们的建议矢量内存架构（VIMA）使用矢量功能单元在3D堆叠的内存附近执行大型矢量说明，并使用小型数据缓存来启用短期数据重复使用。它提供了一个简单的编程界面，并保证了精确的例外。当使用单个核心执行溪流行为的应用程序时，VIMA在CPU系统基线上提供高达26倍的速度，而单核处理器中的矢量操作，同时花费93％的能量。
Data movement is one of the main challenges of contemporary system architectures. Near-Data Processing (NDP) mitigates this issue by moving computation closer to the memory, avoiding excessive data movement. Our proposal, Vector-In-Memory Architecture(VIMA), executes large vector instructions near 3D-stacked memories using vector functional units and uses a small data cache to enable short-term data reuse. It provides an easy programming interface and guarantees precise exceptions. When executing stream-behaved applications using a single core, VIMA offers a speedup of up to 26x over a CPU system baseline with vector operations in a single-core processor while spending 93% less energy.
