@inproceedings{10598,
  abstract     = {{Approximate computing has become a very popular design
strategy that exploits error resilient computations to achieve higher
performance and energy efficiency. Automated synthesis of approximate
circuits is performed via functional approximation, in which various
parts of the target circuit are extensively examined with a library
of approximate components/transformations to trade off the functional
accuracy and computational budget (i.e., power). However, as the number
of possible approximate transformations increases, traditional search
techniques suffer from a combinatorial explosion due to the large
branching factor. In this work, we present a comprehensive framework
for automated synthesis of approximate circuits from either structural
or behavioral descriptions. We adapt the Monte Carlo Tree Search
(MCTS), as a stochastic search technique, to deal with the large design
space exploration, which enables a broader range of potential possible
approximations through lightweight random simulations. The proposed
framework is able to recognize the design Pareto set even with low
computational budgets. Experimental results highlight the capabilities of
the proposed synthesis framework by resulting in up to 61.69% energy
saving while maintaining the predefined quality constraints.}},
  author       = {{Awais, Muhammad and Ghasemzadeh Mohammadi, Hassan and Platzner, Marco}},
  booktitle    = {{26th IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC)}},
  keywords     = {{Approximate computing, High-level synthesis, Accuracy, Monte-Carlo tree search, Circuit simulation}},
  pages        = {{219--224}},
  title        = {{{An MCTS-based Framework for Synthesis of Approximate Circuits}}},
  doi          = {{10.1109/VLSI-SoC.2018.8645026}},
  year         = {{2018}},
}