Stochastic Computing for Reliable Memristive In-Memory Computation
Stochastic Computing for Reliable Memristive In-Memory Computation
复制标题
DOI：
10.1145/3583781.3590307
复制
发表时间：
2023-06
期刊：
影响因子：
--
通讯作者：
Mohsen Riahi Alam;M. Najafi;N. Taherinejad;M. Imani;Lu Peng
中科院分区：
文献类型：
--
作者：
Mohsen Riahi Alam;M. Najafi;N. Taherinejad;M. Imani;Lu Peng
内存计算（IMC）是一个有前途的计算范式，可加速大数据应用程序。它减少了内存和处理单元之间的数据运动，并提供了巨大的并行性。回忆技术是IMC的有前途技术之一。但是，这项新兴技术仍在进化中，面临实际挑战。在存储数据和计算过程中，回忆的记忆容易容易出现。在Memristive IMC中常用的传统二进制编码对软错误敏感，这使得开发可靠的回忆IMC更具挑战性。随机计算（SC）是一种重新出现的计算范式，对软纠正非常强大，因为任何位翻转都会导致至少有显着的位误差。在这项工作中，我们将SC研究作为提高备忘录IMC可靠性的解决方案。我们研究了SC如何以及在多大程度上可以解决或改善当前的回忆技术以及回忆性IMC的可靠性问题。我们还评估了由回忆随机性IMC与传统可靠性技术的特征进行比较。
In-Memory Computing (IMC) is a promising computing paradigm to accelerate Big Data applications. It reduces the data movement between memory and processing units, and provides massive parallelism. Memristive technology is one of the promising technologies for IMC. This emerging technology, however, is still in evolution, facing practical challenges. Memristive memories are prone to softerror while storing the data and during computations. The traditional binary encoding commonly used in memristive IMC is highly sensitive to soft-errors, which makes developing reliable memristive IMC more challenging. Stochastic Computing (SC) is a re-emerging computing paradigm that is highly robust against soft-errors as any bit flip leads to only a least significant bit error. In this work, we study SC as a solution to increase the reliability of memristive IMC. We investigate how and to what extent SC may address or improve the reliability issues of current memristive technology, and memristive IMC. We also evaluate the characteristics yielded by memristive stochastic IMC and compare them with those of the traditional reliability techniques.