A Column Reduction Technique for an In-Memory Machine-Learning Classifier
Jiazhen Xi, H. Yamauchi · International Journal of Machine Learning and Computing · 2018
A column reduction technique for an in-memory machine-learning classifier in 6T SRAM cells is discussed in this paper, based on an error-tolerant boosting algorithm (a.k.a., error-adaptive classifier boosting, EACB).The proposed technique is mainly applied to the in-memory machine-learning classifier system wherein the weight of the linear model is restricted to 1 bit applicable for standard 6T SRAM cells, employing the EACB algorithm to recognize downsampled handwritten digits.First, the number of columns of the boosted classifier is pruned.Second, three methods: greedy search, fast version of greedy search, and worst-care optimization, are discussed and implemented.Finally, the reduction effects of the proposed methods are compared.The simulation results show that besides the 11.50% column reduction from pruning, the proposed methods can further reduce 3.23%, 5.14%, and 5.49% of the column number on average, respectively, with a similar accuracy to ensure that the corresponding part of the model can be reduced to achieve better energy saving.