The complexity of the computational problems is rising faster than the computational platforms' capabilities. This forces researchers to find alternative paradigms and methods for efficient computing. One promising paradigm is accelerating compute-intensive kernels using in-memory computing accelerators since memory is the major bottleneck that limits the amount of parallelism and performance of a system and dominates energy consumption in computation. Leveraging the memory intensive nature of big data applications, an in-memory-based computation system can be presented where logic can be replaced by memory structures, virtually eliminating the need for memory load/store operations during computation. The massive parallelism enabled by such a paradigm results in highly scalable structures.

The present thesis is studied against this background. The objective is to conduct a broad perspective research on in-memory computing. For this purpose, associative computing architectures (i.e., Associative Processors, or AP) are built by both traditional (SRAM) and emerging (ReRAM) memory technologies together with their corresponding software frameworks. For ReRAM-based APs, the reliability concerns coming with the emerging memories are resolved. Architectural innovations are developed to increase the energy efficiency. Furthermore, approximate computing approach is introduced for APs to perform efficient/low-power approximate in-memory computing for the tasks which can tolerate some accuracy lost. The works also propose a novel two-dimensional in-memory computing architecture to cope with the existing deficiencies of the traditional one-dimensional AP architectures.