A coding scheme for reliable in-memory hamming distance computation
A coding scheme for reliable in-memory hamming distance computation
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DOI：
10.1109/acssc.2017.8335653
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发表时间：
2017-10
期刊：
影响因子：
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通讯作者：
Zehui Chen;Clayton Schoeny;Yuval Cassuto;L. Dolecek
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文献类型：
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作者：
Zehui Chen;Clayton Schoeny;Yuval Cassuto;L. Dolecek
内存计算是一种在减轻大规模数据处理负担方面显示出巨大潜力的技术。允许进行超快速的锤距计算进行内存，这将大大加快许多现代的机器学习算法。但是，这些内存计算尚未在过程变化存在下进行研究。在本文中，我们开发了编码方案，以可靠地计算出在写入时间误差的情况下对矢量对的锤子距离。使用反演编码技术，我们建立错误检测保证是错误的数量和存储数据的电阻阵列内存的非理想性的函数。为了纠正向量相似性比较中的错误，我们提出了实现误差校正和有用技术的代码，以实现位级别数据访问和错误本地化的有用技术。我们使用k-nearest邻居算法的简单示例演示了我们的编码方案的有效性。
Computation-in-memory is a technique that has shown great potential in reducing the burden of massive data processing. Allowing for ultra-fast Hamming distance computations to be performed in-memory will drastically speed up many modern machine-learning algorithms. However, these in-memory calculations have not been studied in the presence of process variabilities. In this paper, we develop coding schemes to reliably compute, in-memory, the Hamming distances of pairs of vectors in the presence of write-time errors. Using an inversion coding technique, we establish error-detection guarantees as a function of the number of errors and the non-ideality of the resistive array memory in which the data is stored. To correct errors in the vector similarity comparison, we propose codes that achieve error correction and useful techniques for bit level data access and error localization. We demonstrate the effectiveness of our coding scheme on a simple example using the k-nearest neighbors algorithm.