Browsing by Author "Jiang, JH"

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A variable partitioning algorithm of BDD for FPGA technology mapping
(1997-10-01)
Jiang, JH
;
Jou, JY
;
Huang, JD
;
Wei, JS
;
交大名義發表
Field Programmable Gate Arrays (FPGA's) are important devices for rapid system prototyping. Roth-Karp decomposition is one of the most popular decomposition techniques for Look-Up Table (LUT)-based FPGA technology mapping. In this paper, we propose a novel algorithm based on Binary Decision Diagrams (BDD's) for selecting good lambda set variables in Roth-Karp decomposition to minimize the number of consumed configurable logic blocks (CLB's) in FPGA's. The experimental results on a set of benchmarks show that our algorithm can produce much better results than the similar works of the previous approaches [1], [4].
BDD based lambda set selection in Roth-Karp decomposition for LUT architecture
(1996)
Jiang, JH
;
Jou, JY
;
Huang, JD
;
Wei, JS
;
交大名義發表
Field Programmable Gate Arrays (FPGA's) are important devices for rapid system prototyping. Roth-Karp decomposition is one of the most popular decomposition techniques for Look-Up Table (LUT)-based FPGA technology mapping. In this paper, we propose a novel algorithm based on Binary Decision Diagrams (BDD's) for selecting good lambda set variables in Roth-Karp decomposition to minimize the number of consumed configurable logic blocks (CLB's) in FPGA's. The experimental results on a set of benchmarks show that our algorithm can produce much better results than those of the previous approach [1].
Unified functional decomposition via encoding for FPGA technology mapping
(2001-04-01)
Jiang, JH
;
Jou, JY
;
Huang, JD
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
Functional decomposition has recently been adopted for look-up tabel (LUT)-based field-programmable gate array (FPGA) technology mapping with good results. In this paper we propose a novel method to unify functional single;output and multiple-output decomposition. We first address a compatible class encoding algorithm to minimize the number of compatible classes in the image function. After applying the encoding algorithm, we can therefore improve the decomposability in the subsequent decomposition of the; image function. The above encoding algorithm is then extended to encode multiple-output functions through the construction of a hyperfunction, Common subexpressions among these multiple-output functions can be extracted during the decomposition of the hyperfunction, Consequently, we can handle multiple-output decomposition in the same manner as single-output decomposition. Experimental results show that our algorithms are promising.