A coding scheme for reliable in-memory hamming distance computation

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.