  Content addressable memory (CAM) stands out as an efficient hardware solution
for memory-intensive search operations by supporting parallel computation in
memory. However, developing a CAM-based accelerator architecture that achieves
acceptable accuracy, while minimizing hardware cost and catering to both exact
and approximate search, still presents a significant challenge especially when
considering a broader spectrum of applications. This complexity stems from
CAM's rapid evolution across multiple levels--algorithms, architectures,
circuits, and underlying devices. This paper introduces CAMASim, a first
comprehensive CAM accelerator simulation framework, emphasizing modularity,
flexibility, and generality. CAMASim establishes the detailed design space for
CAM-based accelerators, incorporates automated functional simulation for
accuracy, and enables hardware performance prediction, by leveraging a
circuit-level CAM modeling tool. This work streamlines the design space
exploration for CAM-based accelerator, aiding researchers in developing
effective CAM-based accelerators for various search-intensive applications.
