Performing analysis on large graph datasets in an energy-efficient manner has posed a significant challenge; not only due to excessive data movements and poor locality, but also due to the non-optimal use of high sparsity of such datasets. The latter leads to a waste of resources as the computation is also performed on zero's operands which do not contribute to the final result. This paper designs a novel graph processing accelerator, SparseMEM, targeting sparse datasets by leveraging the computing-in-memory (CIM) concept; CIM is a promising solution to alleviate the overhead of data movement and the inherent poor locality of graph processing. The proposed solution stores the graph information in a compressed hierarchical format inside the memory and adjusts the workflow based on this new mapping. This vastly improves resource utilization, leading to higher energy and permanence efficiency. The experimental results demonstrate that SparseMEM outperforms a GPU-based platform and two state-of-the-art in-memory accelerators on speedup and energy efficiency by one and three orders of magnitude, respectively.@en

In recent years, we are witnessing a trend moving away from conventional computer architectures towards Computation-In-Memory (CIM) based on emerging memristor devices. This is due to the fact that the performance and energy efficiency of traditional computer architectures can no longer be increased at the same pace as before. The main barriers which limit the performance and energy improvement are the memory and power walls. Thus far, the main effort from researchers is toward enabling CIM as an accelerator for specific applications. Consequently, this current application-specific nature/approach has put less emphasis on the potential general-purpose applicability of CIM, i.e., merging several accelerators into one that is less than the sum of the parts. In this paper, we demonstrate the CIM concept using a broader and generalized model. Considering this model, the state-of-the-art CIM-based logic and arithmetic primitive functions, which can be the building blocks for complex functions, are investigated. Besides, we present potential applications of CIM which provides insights into the challenges and opportunities of a generic CIM system design. Finally, we highlight the future directions regarding the construction of CIM-based systems.@en