In-Memory Intelligence
In-Memory Intelligence
复制标题
内存中智能
作者：
Timothy P. Finkbeiner;Glen Hush;Troy Larsen;Perry Lea;John D. Leidel;Troy Manning
近数据处理的最新活动已经建立或提出了可以利用诸如3D堆栈、原位计算或可编程设备等技术的系统。然而，很少有人努力利用DRAM的自然并行性和吞吐量。本文详细介绍了Micron Technology在内存处理领域的研究，这是一种以内存为中心的计算形式。内存智能（IMI）试图将大量的位串行计算元件阵列与内存阵列放在一起，尽可能接近信息。这与依赖于某种形式的存储但必须通过快速、低延迟接口与该存储通信的近存储器设备形成对比。初始模拟和模型显示了各种应用在性能和功率方面的阶梯式改进。这种技术允许DRAM在异构系统中提供功能，以减轻冯-诺依曼势垒的压力。
Recent activity in near-data processing has built or proposed systems that can exploit technologies such as 3D stacks, in-situ computing, or dataflow devices. However, little effort has been applied to exploit the natural parallelism and throughput of DRAM. This article details research from Micron Technology in the area of processing in memory as a form of memory-centric computing. In-Memory Intelligence (IMI) attempts to place a massive array of bit-serial computing elements on pitch with the memory array, as close to the information as possible. This contrasts with near-memory devices that rely on some form of storage but must communicate with that storage via a fast, low-latency interface. Initial simulations and models show stair-step improvements in performance and power for various applications. Such technology allows DRAM to provide functionality in a heterogeneous system to alleviate the pressures of the von-Neumann barrier.