Dynamic Memory Design for Low Data-Retention Power
Dynamic Memory Design for Low Data-Retention Power
复制标题
低数据保留功耗的动态内存设计
DOI：
10.1007/3-540-45373-3_22
复制
发表时间：
2000
影响因子：
3.7
通讯作者：
M. Papaefthymiou
中科院分区：
文献类型：
--
作者：
Joohee Kim;M. Papaefthymiou
移动的环境中数据密集型应用的出现导致了具有越来越大的动态存储器的便携式电子系统。这些应用程序所表现出的典型操作模式是一个相对较短的操作突发，随后是较长的待机时间。由于它们的周期性刷新要求，动态存储器即使在待机期间也消耗大量功率，因此对电池寿命具有显著影响。
在本文中，我们研究了一种方法，设计动态存储器与低数据保持能力。我们的方法依赖于这样一个事实，即存储器阵列的刷新周期是由只有几个，最坏情况下的泄漏单元。在我们的方案中，多个刷新周期被用来通过选择性地只刷新即将丢失其存储值的单元来减少能量耗散。通过使用纠错来恢复损坏的单元值，从而允许延长的刷新周期，实现了额外的能量节省。我们描述了一个精确的O（nk-1）时间算法，给定一个存储器阵列与n个刷新块和两个正整数k和l，计算k刷新周期，最大化的平均刷新周期的存储器阵列刷新时，发生在块的l个单元。在模拟与16 Mb的存储器阵列和（72，64）修改的汉明单纠错码，我们的计划的结果在平均刷新周期长达11倍，比原来的刷新周期。
The emergence of data-intensive applications in mobile environments has resulted in portable electronic systems with increasingly large dynamic memories. The typical operating pattern exhibited by these applications is a relatively short burst of operations followed by longer periods of standby. Due to their periodic refresh requirements, dynamic memories consume substantial power even during standby and thus have a significant impact on battery lifetime.
In this paper we investigate a methodology for designing dynamic memory with low data-retention power. Our approach relies on the fact that the refresh period of a memory array is dictated by only a few, worst-case leaky cells. In our scheme, multiple refresh periods are used to reduce energy dissipation by selectively refreshing only the cells that are about to lose their stored values. Additional energy savings are achieved by using error-correction to restore corrupted cell values and thus allow for extended refresh periods. We describe an exact O(nk-1)-time algorithm that, given a memory array with n refresh blocks and two positive integers k and l, computes k refresh periods that maximize the average refresh period of a memory array when refreshing occurs in blocks of l cells. In simulations with 16Mb memory arrays and a (72, 64) modified Hamming single-error correction code, our scheme results in an average refresh period of up to 11 times longer than the original refresh period.