Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
Algorithm 457: finding all cliques of an undirected graph
Communications of the ACM
Automatic application-specific instruction-set extensions under microarchitectural constraints
Proceedings of the 40th annual Design Automation Conference
Processor Acceleration Through Automated Instruction Set Customization
Proceedings of the 36th annual IEEE/ACM International Symposium on Microarchitecture
Application-specific instruction generation for configurable processor architectures
FPGA '04 Proceedings of the 2004 ACM/SIGDA 12th international symposium on Field programmable gate arrays
Algorithmic Graph Theory and Perfect Graphs (Annals of Discrete Mathematics, Vol 57)
Algorithmic Graph Theory and Perfect Graphs (Annals of Discrete Mathematics, Vol 57)
ISEGEN: Generation of High-Quality Instruction Set Extensions by Iterative Improvement
Proceedings of the conference on Design, Automation and Test in Europe - Volume 2
Application Specific Datapath Extension with Distributed I/O Functional Units
VLSID '07 Proceedings of the 20th International Conference on VLSI Design held jointly with 6th International Conference: Embedded Systems
Polynomial-time subgraph enumeration for automated instruction set extension
Proceedings of the conference on Design, automation and test in Europe
Rethinking custom ISE identification: a new processor-agnostic method
CASES '07 Proceedings of the 2007 international conference on Compilers, architecture, and synthesis for embedded systems
An Algorithm for Finding Input-Output Constrained Convex Sets in an Acyclic Digraph
Graph-Theoretic Concepts in Computer Science
Fast custom instruction identification by convex subgraph enumeration
ASAP '08 Proceedings of the 2008 International Conference on Application-Specific Systems, Architectures and Processors
Better Than Optimal: Fast Identification of Custom Instruction Candidates
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 02
A polynomial-time custom instruction identification algorithm based on dynamic programming
Proceedings of the 16th Asia and South Pacific Design Automation Conference
Custom-instruction synthesis for extensible-processor platforms
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Exact and approximate algorithms for the extension of embedded processor instruction sets
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Fast Identification of Custom Instructions for Extensible Processors
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
CHIPS: Custom Hardware Instruction Processor Synthesis
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
FISH: Fast Instruction SyntHesis for Custom Processors
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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Synthesis of custom instruction processors from high-level application descriptions involves automated evaluation of data-flow subgraphs as custom instruction candidates. A subgraph S of a graph D, is convex if no two vertices of S are connected by a path in D that is not also in S. An algorithm for enumerating all convex subgraphs of a directed acyclic graph (DAG) under input, output, and forbidden vertex constraints was given by Pozzi, Atasu, and Ienne. We show that this algorithm makes no more than O(|V(D)|Nin+Nout+1) recursive calls, where |V(D)| is the number of vertices in D, and Nin and Nout are input and output constraints, respectively. Therefore, when Nin and Nout are constants, the algorithm is of polynomial complexity. Furthermore, a convex subgraph S is a maximal convex subgraph if it is not a proper subgraph of some other convex subgraph, assuming that both are valid under forbidden vertex constraints. The largest maximal convex subgraph is called the maximum convex subgraph. There exist popular algorithms that enumerate maximal convex subgraphs, which all have exponential worst-case time complexity. This work shows that although no polynomial-time maximal convex subgraph enumeration algorithm can exist, the related maximum convex subgraph problem can be solved in polynomial time.