Due to the significance and broad utilization of adders in computing systems, the design of low-power approximate adders (LPAAs) has received a significant amount of attention from the system design community. However, the selection and deployment of appropriate approximate modules require a thorough design space exploration, which is (in general) an extremely time-consuming process. Toward reducing the exploration time, different error estimation techniques have been proposed in the literature for evaluating the quality metrics of approximate adders. However, most of them are based on certain assumptions that limit the usability of such techniques for real-world settings. In this work, we highlight the impact of those assumptions on the quality of error estimates provided by the state-of-the-art techniques and how they limit the use of such techniques for real-world settings. Moreover, we highlight the significance of considering input data characteristics to improve the quality of error estimation. Based on our analysis, we propose a systematic data-driven error estimation methodology, DREAMx, for adders composed of cascaded approximate units, which covers a predominant set of LPAAs. DREAMx in principle factors in the dependence between input bits based on the given input distribution to compute the probability mass function (PMF) of error value at the output of an approximate adder. It achieves improved results compared to the state-of-the-art techniques while offering a substantial decrease in the overall execution(/exploration) time compared to exhaustive simulations. Our results further show that there exists a delicate tradeoff between the achievable quality of error estimates and the overall execution time.

Machine Learning (ML) architectures have been applied to several applications that involve sensitive data, where a guarantee of users' data privacy is required. Differentially Private Stochastic Gradient Descent (DPSGD) is the state-of-the-art method to train privacy-preserving models. However, DPSGD comes at a considerable accuracy loss leading to sub-optimal privacy/utility trade-offs. Towards investigating new ground for better privacy-utility trade-off, this work questions; (i) if models' hyperparameters have any inherent impact on ML models' privacy-preserving properties, and (ii) if models' hyperparameters have any impact on the privacy/utility trade-off of differentially private models. We propose a comprehensive design space exploration of different hyperparameters such as the choice of activation functions, the learning rate and the use of batch normalization. Interestingly, we found that utility can be improved by using Bounded RELU as activation functions with the same privacy-preserving characteristics. With a drop-in replacement of the activation function, we achieve new state-of-the-art accuracy on MNIST (96.02%), FashionMnist (84.76%), and CIFAR-10 (44.42%) without any modification of the learning procedure fundamentals of DPSGD.