Static scheduling of synchronous data flow programs for digital signal processing
IEEE Transactions on Computers
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DAC '87 Proceedings of the 24th ACM/IEEE Design Automation Conference
Joint Minimization of Code and Data for Synchronous DataflowPrograms
Formal Methods in System Design
Memory efficient software synthesis form dataflow graph
Proceedings of the 11th international symposium on System synthesis
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Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
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ASAP '97 Proceedings of the IEEE International Conference on Application-Specific Systems, Architectures and Processors
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Shared buffer implementations of signal processing systems using lifetime analysis techniques
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Proceedings of the 2005 international conference on Compilers, architectures and synthesis for embedded systems
Memory optimal single appearance schedule with dynamic loop count for synchronous dataflow graphs
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Buffer memory optimization for video codec application modeled in Simulink
Proceedings of the 43rd annual Design Automation Conference
Integration, the VLSI Journal
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In multimedia and graphics applications, data samples of nonprimitive type require significant amount of buffer memory. This paper addresses the problem of minimizing the buffer memory requirement for such applications in embedded software synthesis from graphical dataflow programs based on the synchronous dataflow (SDF) model with the given execution order of nodes. We propose a memory minimization technique that separates global memory buffers from local pointer buffers: the global buffers store live data samples and the local buffers store the pointers to the global buffer entries. The proposed algorithm reduces 67% memory for a JPEG encoder, 40% for an H.263 encoder compared with unshared versions, and 22% compared with the previous sharing algorithm for the H.263 encoder. Through extensive buffer sharing optimization, we believe that automatic software synthesis from dataflow program graphs achieves the comparable code quality with the manually optimized code in terms of memory requirement.