Convolutional Neural Networks (CNNs) are widely used in vision-related tasks and require intensive computation, due to the large number of multiplications in their convolutional layers. Their inherent tolerance to small numerical perturbations makes them well-suited for approximate computing, which can significantly reduce circuit area and energy consumption while having a limited impact on accuracy. We present an approach for generating approximate multipliers tailored to CNN input distributions. By using multiple complementary constraints and integrating them into an SMT-based design framework, our method effectively explores the approximation design space, producing multipliers that achieve an effective accuracy–efficiency tradeoff. Compared to five state-of-the-art CNN-oriented design techniques, our approach reduces PDA (Power-Delay-Area product) by an average of 17.45% (up to 25.73%) at equivalent accuracy.

This paper presents a novel iterative approach to achieve effective and efficient approximate logic synthesis (ALS). The core idea is to perform circuit rewriting in a way that is both local, i.e., is applied piece-wise to selected subcircuits, and extensive, i.e., systematically explores the design space for good solutions. Concretely, we propose SubXPAT, a new Boolean rewriting framework which iteratively employs satisfiability modulo theories (SMT) solving to select and approximate key parts of a circuit. Selection aims at finding subcircuits that at the same time include a significant number of gates and can be efficiently approximated, which is done by searching for large convex subcircuits with a limited number of inputs and outputs. Approximation is guided by the use of a parametric template, structured as a sum of products, which allows for fine-grained control over the subcircuit characteristics. SubXPAT was implemented as an open-source tool and compared against other ALS tools implementing state-of-the-art techniques. Our experimental evaluation used a broad range of arithmetic circuits with different bit-widths and our results indicate that SubXPAT generates approximate circuits that are more area-efficient than those generated by state-of-the-art techniques in 72% of the cases.