TY - GEN
T1 - A coding scheme for reliable in-memory hamming distance computation
AU - Chen, Zehui
AU - Schoeny, Clayton
AU - Cassuto, Yuval
AU - Dolecek, Lara
N1 - Funding Information: ACKNOWLEDGMENT Research supported in part by a grant from UC MEXUS and an NSF-BSF grant no.1718389. V. CONCLUSION This paper provides coding schemes for error detection and error correction in order to facilitate reliable in-memory Hamming distance computation under write noise due to power constrains. Future efforts includes the analysis when the channel parameter ϵ is not a fixed constant and extended analysis on other machine learning algorithms. REFERENCES Publisher Copyright: © 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85050997127
U2 - 10.1109/ACSSC.2017.8335653
DO - 10.1109/ACSSC.2017.8335653
M3 - Conference contribution
T3 - Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
SP - 1713
EP - 1717
BT - Conference Record of the 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
A2 - Matthews, Michael B.
T2 - 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Y2 - 29 October 2017 through 1 November 2017
ER -