Error-Detection Schemes for Analog Content-Addressable Memories

Analog content-addressable memories (in short, a-CAMs) have been recently introduced as accelerators for machine-learning tasks, such as tree-based inference or implementation of nonlinear activation functions. The cells in these memories contain nanoscale memristive devices, which may be susceptible to various types of errors, such as manufacturing defects, inaccurate programming of the cells, or drifts in their contents over time. The objective of this work is to develop techniques for overcoming the reliability issues that are caused by such error events. To this end, several coding schemes are presented for the detection of errors in a-CAMs. These schemes consist of an encoding stage, a detection cycle (which is performed periodically), and some minor additions to the hardware. During encoding, redundancy symbols are programmed into a portion of the a-CAM (or, alternatively, are written into an external memory). During each detection cycle, a certain set of input vectors is applied to the a-CAM. The schemes differ in several ways, e.g., in the range of alphabet sizes that they are most suitable for, in the tradeoff that each provides between redundancy and hardware additions, or in the type of errors that they handle (Hamming metric versus $L_{1}$ metric).