Getao Liang

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Cholesky decomposition has been widely utilized for positive symmetric matrix factorization in solving least square problems. Various parallel accelerators including GPUs and FPGAs have been explored to improve performance. In this paper, Cholesky decomposition is implemented on both FPGAs and GPUs by designing a dedicated architecture for FPGAs and(More)
The Relevance Vector Machine (RVM) algorithm has been widely utilized in many applications, such as machine learning, image pattern recognition, and compressed sensing. However, the RVM algorithm is computationally expensive. We seek to accelerate the RVM algorithm computation for time sensitive applications by utilizing massively parallel accelerators such(More)
Exploiting computational precision can improve performance significantly without losing accuracy in many applications. To enable this, we propose an innovative arithmetic logic unit (ALU) architecture that supports true dynamic precision operations on the fly. The proposed architecture targets both fixed-point and floating-point ALUs, but in this paper we(More)
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