| Autor: |
Guo, C, Luk, W, Warren, A, Levine, J, Brookes, P |
| Rok vydání: |
2023 |
| Zdroj: |
IEEE International Conference on Application-specific Systems, Architectures, and Processors |
| Popis: |
Conditional independence (CI) testing is a critical statistical method that determines conditional independence between variables using data. It is useful for various data mining applications, such as causal discovery, Bayesian inference, and agent-based model validation. However, the high volume of CI test queries and the large data sizes make CI testing computationally intensive. This paper proposes a hardware-oriented residual-based CI testing algorithm, co-designed with an FPGA accelerator, to address this issue. Our system accelerates CI tests by skipping least-squares computations algorithmically, enabling fixed-point operations in correlation evaluation and parallelization of permutation tests. Our experimental evaluation demonstrates that our method is as accurate as state-of-the-art CI testing approaches. Furthermore, our experimental implementation on an Intel Arria 10 FPGA delivers up to 32 times higher performance compared to state-of-the-art CI test tools running on eight Intel Xeon Silver 4110 CPU cores. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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