Adaptive Block Error Correction for Memristive Crossbars

Abstract

Matrix-vector multiplication (MVM) is one of the most frequent operations performed in deep learning and big data applications. On the other hand, the Memory wall problem in traditional processor-centric architectures limits the performance of these applications. The crossbar array of emerging non-volatile memristive devices (memristive crossbar) provides an energy-efficient hardware implementation of MVM for deep learning accelerators and edge computing hardware. However, non-idealities as well as manufacturing and runtime defects of the memristive devices may severely impact the reliability of target applications. This paper presents a new online block error correction technique for memristive crossbars. It enables reliable MVM computation by combining the idea of checksum and Hamming code-based linear coding scheme. The proposed method can correct any number of errors in one particular array block containing multiple columns. An adaptive error correction coding strategy is also presented, so that the ratio of data columns to the parity checksum columns can be adjusted at runtime based on the fault rate, enabling the optimum use of data and parity checksum columns.