High-level synthesis: introduction to chip and system design
High-level synthesis: introduction to chip and system design
Computing lower bounds on functional units before scheduling
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Behavioral synthesis: digital system design using the synopsys behavioral compiler
Behavioral synthesis: digital system design using the synopsys behavioral compiler
Recent developments in high-level synthesis
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Statistical analysis of extreme values
Statistical analysis of extreme values
Synthesis and Optimization of Digital Circuits
Synthesis and Optimization of Digital Circuits
Lookup Table Power Macro-Models for Behavioral Library Components
VOLTA '99 Proceedings of the IEEE Alessandro Volta Memorial Workshop on Low-Power Design
SCALP: an iterative-improvement-based low-power data path synthesis system
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
High-level synthesis of low-power control-flow intensive circuits
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Efficient design exploration based on module utility selection
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Methods for evaluating and covering the design space during early design development
Integration, the VLSI Journal
A genetic algorithm high-level optimizer for complex datapath and data-flow digital systems
Applied Soft Computing
Predicting best design trade-offs: a case study in processor customization
DATE '12 Proceedings of the Conference on Design, Automation and Test in Europe
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The capability of performing semi-automated design space exploration is the main advantage of high-level synthesis with respect to RTL design. However, design space exploration performed during high-level synthesis is limited in scope, since it provides promising solutions that represent good starting points for subsequent optimizations, but it provides no insight about the overall structure of the design space. In this work we propose unsupervised Monte-Carlo design exploration and statistical characterization to capture the key features of the design space. Our analysis provides insight on how various solutions are distributed over the entire design space. In addition, we apply extreme value theory [11] to extrapolate achievable bounds from the sampling points.