Multi-sensor fusion using LiDAR and RGB cameras significantly enhances 3D object detection task. However, conventional LiDAR sensors perform dense, stateless scans, ignoring the strong temporal continuity in real-world scenes. This leads to substantial sensing redundancy and excessive power consumption, limiting their practicality on resource-constrained platforms. To address this inefficiency, we propose a predictive, history-aware adaptive scanning framework that anticipates informative regions of interest (ROI) based on past observations. Our approach introduces a lightweight predictor network that distills historical spatial and temporal contexts into refined query embeddings. These embeddings guide a differentiable mask generator network, which leverages Gumbel-Softmax sampling to produce binary masks identifying critical ROIs for the upcoming frame. Our method significantly reduces unnecessary data acquisition by concentrating dense LiDAR scanning only within these ROIs and sparsely sampling elsewhere. Experiments on nuScenes and Lyft benchmarks demonstrate that our adaptive scanning strategy reduces LiDAR energy consumption by over 65% while maintaining competitive or even superior 3D object detection performance compared to traditional LiDAR-camera fusion methods with dense LiDAR scanning.

Automatic synthesis of analog and Radio Frequency (RF) circuits is a trending approach that requires an efficient circuit modeling method. This is due to the expensive cost of running a large number of simulations at each synthesis cycle. Artificial intelligence methods are promising approaches for circuit modeling due to their speed and relative accuracy. However, existing approaches require a large amount of training data, which is still collected using simulation runs. In addition, such approaches collect a whole separate dataset for each circuit topology even if a single element is added or removed. These matters are only exacerbated by the need for post-layout modeling simulations, which take even longer. To alleviate these drawbacks, in this paper, we present FuNToM, a functional modeling method for RF circuits. FuNToM leverages the two-port analysis method for modeling multiple topologies using a single main dataset and multiple small datasets. It also leverages neural networks which have shown promising results in predicting the behavior of circuits. Our results show that for multiple RF circuits, in comparison to the state-of-the-art works, while maintaining the same accuracy, the required training data is reduced by 2.8x - 10.9x. In addition, FuNToM needs 176.8x - 188.6x less time for collecting the training set in post-layout modeling.

Machine Learning (ML) has shown promising results in predicting the behavior of analog circuits. However, in order to completely cover the design space for today’s complicated circuits, supervised ML requires a large number of labeled samples which is time-consuming to provide. Furthermore, a separate dataset must be collected for each circuit topology making all other previously gathered datasets useless. In this paper, we first present a database including labeled and unlabeled data. We use neural networks to determine the behavior of complicated topologies by combining the more simple ones. By generating such unlabeled data, the time for providing the training set is significantly reduced compared to the conventional approaches. Using this database, we propose a fully-automated analog circuit generator framework, AnGeL. AnGeL performs all the schematic circuit design steps from deciding the circuit topology to determining the circuit parameters i.e. sizing. Our results show that for multiple circuit topologies, in comparison to the state-of-the-art works while maintaining the same accuracy, the required labeled data is reduced by 4.7x - 1090x. Also, the runtime of AnGeL is 2.9x - 75x faster.