Automatic Approximation of Computer Systems Through Multi-objective Optimization
Abstract
In this chapter, we address the automatic approximation of computer systems through multi-objective optimization. Firstly, we present our automatic design methodology, i.e., how we model the approximate design space to be automatically explored. The exploration is achieved through multi-objective optimization to find good trade-offs between the system efficiency and accuracy. Then, we show how the methodology is applied to the systematic and application-independent design of generic combinational logic circuits, based on non-trivial local rewriting of and-inverter graphs (AIGs). Finally, to push forward the approximation limits, we showcase the design of approximate hardware accelerators for image processing and for common machine-learning-based classification models.
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