For a very long period of time, computing could meet the increasing demands of different applications due to the continued downscaling of transistors, which allowed data to be processed at a higher frequency. In the early 2000s, predictions about the physical limits and rising costs of continued down scaling prompted researchers to adopt alternative techniques to sustain performance improvements beyond frequency scaling. Among these, the most prevalent technique was the extraction and utilization of parallelism, which successfully extended performance scaling for more than a decade but has since begun to stagnate. Today, experts agree that specialized complementary hardware is crucial for further advancements. Computing-in-memory (CIM) accelerators are gaining traction as an innovative solution to the problems conventional computing is facing. While most CIM research is directed towards device, circuit, and architectural level challenges, it is also important to consider the challenges at the programming level. In this chapter, we first discuss the motivation behind CIM accelerators and why developing a programming model for them is essential. Next, we provide an overview of the challenges associated with developing a dedicated programming model for this emerging technology. Finally, we will outline the research direction of this thesis.

The rapid advancement of Quantum Network architectures necessitates a comprehensive and quantitative comparison to assess their effectiveness and performance. Unfortunately, there does not exist an implemented quantum network benchmark suite capable of determining the superior architecture. Hence, our study aims to establish the foundation for developing a benchmark suite by leveraging existing quantum network applications. However, the specific inclusion of quantum network applications in the suite remains to be determined. Therefore, to address this gap, our study will explore the potential inclusion of the Clauser-Horne-Shimony-Holt (CHSH) game based on its effectiveness in identifying errors within various properties of the quantum networking system. We use an exploratory research methodology involving experiments performed on simulated quantum networks utilizing SquidASM. Each experiment simulates multiple quantum networks, with a single property as the independent variable. For each value of the independent variable, we calculate both the success probability of the game and the number of successes per second. Subsequently, we employ the one-way ANOVA test to examine if there are significant variations in these performance metrics. Our results demonstrate that the CHSH game exhibits sensitivity to all properties affecting the quality of entanglement between nodes, execution time, and the error probability of both single-qubit gates and measure operations. Additionally, we compare the success probabilities based on different input combinations using the Root Mean Squared metric to uncover any underlying patterns within the data. As a result, we discovered a procedure for quantifying the difference between the error probabilities of measurements of zero and one. Based on the outcomes of our study, we consider the CHSH game to be a suitable addition to the benchmark suite if the testing requirements of the suite align with the qualities offered by the application. We anticipate that these results will aid the development of the benchmark suite and advance the understanding of quantum network architectures and their evaluation