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Machine learning and data analytics applications increasingly suffer from thehigh latency and energy consumption of conventional von Neumann architectures.Recently, several in-memory and near-memory systems have been proposed toremove this von Neumann bottleneck. Platforms based on content-addressablememories (CAMs) are particularly interesting due to their efficient support forthe search-based operations that form the foundation for many applications,including K-nearest neighbors (KNN), high-dimensional computing (HDC),recommender systems, and one-shot learning among others. Today, these platformsare designed by hand and can only be programmed with low-level code, accessibleonly to hardware experts. In this paper, we introduce C4CAM, the first compilerframework to quickly explore CAM configurations and to seamlessly generate codefrom high-level TorchScript code. C4CAM employs a hierarchy of abstractionsthat progressively lowers programs, allowing code transformations at the mostsuitable abstraction level. Depending on the type and technology, CAM arraysexhibit varying latencies and power profiles. Our framework allows analyzingthe impact of such differences in terms of system-level performance and energyconsumption, and thus supports designers in selecting appropriate designs for agiven application.

C4CAM: A Compiler for CAM-based In-memory Accelerators