With the rise of Internet of Things (IoT), low-cost resource-constrained devices have to be more capable than traditional embedded systems, which operate on stringent power budgets. In order to add new capabilities such as learning, the power consumption planning has to be revised. Approximate computing is a promising paradigm for reducing power consumption at the expense of inaccuracy introduced to the computations. In this paper, we set forth approximate computing features of a processor that will exist in the next generation low-cost resource-constrained learning IoT devices. Based on these features, we design an approximate IoT processor which benefits from RISC-V ISA. Targeting machine learning applications such as classification and clustering, we have demonstrated that our processor reinforced with approximate operations can save power up to 23% for ASIC implementation while at least 90% top-1 accuracy is achieved on the trained models and test data set.

Due to the rising of both economic and environmental concerns in the energy sector, each subdivision of the community is investigating new solutions to overcome this critical issue. For this reason, electric vehicles (EVs) have gained more significance in the transportation sector owing to their efficient and clean operation chance. These improvements, however, bring new challenges such as installation costs, infrastructure renovation, and loading of the existing power system. Here, optimal sizing and siting of EV charging stations (CSs) are examined in a mixed‐integer linear programming framework with the aim of minimizing the number of EVCSs in the distribution system (which in turn means to minimize CS‐related investment while satisfying EV owners' needs) while satisfying constraints. The proposed optimization model considers EVCS types with different charging rate capabilities to provide opportunities for demand‐side management. Moreover, the model takes the actual behaviour of the battery charging pattern into account by using real measured EV charging data together with the consideration of an actual distribution system belonging to a region in Turkey. Lastly, a bunch of case studies is conducted in order to validate the accuracy and effectiveness of the devised model.

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