Autonomous robots are expected to perform a wide range of sophisticated tasks in complex,
unknown environments. However, available onboard computing capabilities and algorithms …

Smart cities are transforming by integrating artificial intelligence (AI) drones for various
applications, including traffic monitoring, public space management, and surveillance …

We propose MC-CIM, a compute-in-memory (CIM) framework for robust, yet low power,
Bayesian edge intelligence. Deep neural networks (DNN) with deterministic weights cannot …

This work proposes a novel Energy-aware Network Operator Search (ENOS) approach to
address the energy-accuracy trade-offs of a deep neural network (DNN) accelerator. In …

The edge processing of deep neural networks (DNNs) is becoming increasingly important
due to its ability to extract valuable information directly at the data source to minimize latency …

This paper introduces a lightweight uncertainty estimator capable of predicting multimodal
(disjoint) uncertainty bounds by integrating conformal prediction with a deep-learning …

The increasing complexity of deep learning systems has pushed conventional computing
technologies to their limits. While the memristor is one of the prevailing technologies for …

Aiming at the problem of detecting and locating the leakage position of urban pipelines, an
underwater navigation and positioning method combining the jet link inertial navigation …

Bayesian neural networks (BNNs) have been proposed to address the problems of
overfitting and overconfident decision making, common in conventional neural networks …

In floating gate compute-in-memory (CIM) chips, due to the gate equivalent capacitance of
the large-scale array and the parasitic capacitance of the long-distance transmission wire, it …