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.

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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.

Publications

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 …