A New Class of Geometric Analog Error Correction Codes for Crossbar Based In-Memory Computing
This work addresses a domain-specific problem for analog in-memory computing systems, but appears incremental as it builds on existing geometric codes.
The paper tackles the limited availability of analog error correction codes for in-memory computing by studying a geometric code family for handling multiple outliers, developing a geometric analysis to characterize their m-height profiles.
Analog error correction codes have been proposed for analog in-memory computing on resistive crossbars, which can accelerate vector-matrix multiplication for machine learning. Unlike traditional communication or storage channels, this setting involves a mixed noise model with small perturbations and outlier errors. A number of analog codes have been proposed for handling a single outlier, and several constructions have also been developed to address multiple outliers. However, the set of available code families remains limited, covering only a narrow range of code lengths and dimensions. In this paper, we study a recently proposed family of geometric codes capable of handling multiple outliers, and develop a geometric analysis that characterizes their m-height profiles.