Two Dimensional Associative Processor (2D AP)

The research project, titled “Associative In-Memory Graph Processing Paradigm: Towards Tera-TEPS Graph Traversal In a Box", won the NSF CAREER Award in 2018. In this research, we developed a radically new computing paradigm, namely two-dimensional associative processing (2D AP) to further advance our previous FPGA-based graph processing architectures and fundamentally address their limitations. Mathematically, 2D AP is a new general-purpose computing model that exploits an extra dimension of parallelism (both intra-word and inter-word parallelism) to accelerate computation as compared with traditional AP which only exploit inter-word parallelism. It is particularly beneficial for massive-scale graph processing. For the first time, we provide a theoretical proof that 2D AP is inherently more efficient as measured by “architecturally determined complexity” in runtime/area/energy than both von Neumann architecture and traditional AP paradigm in performing graph computation. We also provide detailed micro-architectures and circuits to best implement the proposed computing model, with domain-special language support. A preliminary published version of 2D AP [Khoram2018CAL] was recognized as best of CAL (IEEE Computer Architecture Letters) in 2018.

Jing Li
Eduardo D. Glandt Faculty Fellow and Associate Professor

Attracted to all the big problems in computer system across the stack regardless the specific sub-areas.


3D-stacking memory technology such as High-Bandwidth Memory (HBM) and Hybrid Memory Cube (HMC) provides orders of magnitude more …

Associative Processing (AP) is a promising alternative to the Von Neumann model as it addresses the memory wall problem through its …