Autonomous edge computing in robotics, smart cities, and autonomous vehicles relies on the seamless integration of sensing, processing, and actuation for real-time decision-making in dynamic environments. At its core is the sensing-to-action loop, which iteratively aligns sensor inputs with computational models to drive adaptive control strategies. These loops can adapt to hyper-local conditions, enhancing resource efficiency and responsiveness, but also face challenges such as resource constraints, synchronization delays in multimodal data fusion, and the risk of cascading errors in feedback loops. This article explores how proactive, context-aware sensing-to-action and action-to-sensing adaptations can enhance efficiency by dynamically adjusting sensing and computation based on task demands, such as sensing a very limited part of the environment and predicting the rest. By guiding sensing through control actions, action-to-sensing pathways can improve task relevance and resource use, but they also require robust monitoring to prevent cascading errors and maintain reliability. Multi-agent sensing-action loops further extend these capabilities through coordinated sensing and actions across distributed agents, optimizing resource use via collaboration. Additionally, neuromorphic computing, inspired by biological systems, provides an efficient framework for spike-based, event-driven processing that conserves energy, reduces latency, and supports hierarchical control-making it ideal for multi-agent optimization. This article highlights the importance of end-to-end co-design strategies that align algorithmic models with hardware and environmental dynamics, improve cross-layer inter-dependencies to improve throughput, precision, and adaptability for energy-efficient edge autonomy in complex environments.

Accurate modeling and prediction of temporal point processes (TPPs) are crucial across domains such as neuroscience, epidemiology, finance, and social media analysis. We introduce the Spiking Dynamic Graph Network (SDGN), which integrates spiking neural networks (SNNs) with local spike-timing-dependent plasticity (STDP) to learn, online and in an event-driven fashion, the evolving spatio-temporal graph underlying a stream of timestamped events. SDGN relies on adaptive time-stepping, surrogate-gradient smoothing, and priority-queue updates to achieve O(log N) complexity per spike, ensuring both stability and efficiency. On synthetic benchmarks and four large-scale real-world datasets (NYC Taxi, 911 dispatches, Reddit posts, and Stack Overflow events), SDGN attains up to 15% higher held-out log-likelihood and 2× faster inference compared to state-of-the-art baselines. Ablation studies quantify the impact of each core component, and we discuss extensions for handling very dense graphs and heavy-tailed inter-event distributions.

Edge platforms in autonomous systems integrate multiple sensors to interpret their environment. The high-resolution and high-bandwidth pixel arrays of these sensors improve sensing quality but also generate a vast, and arguably unnecessary, volume of real-time data. This challenge, often referred to as the analog data deluge, hinders the deployment of high-quality sensors in resource-constrained environments. This paper discusses the concept of cognitive sensing, which learns to extract low-dimensional features directly from high-dimensional analog signals, thereby reducing both digitization power and generated data volume. First, we discuss design methods for analog-to-feature extraction (AFE) using mixed-signal compute-in-memory. We then present examples of cognitive sensing, incorporating signal processing or machine learning, for various sensing modalities including vision, Radar, and Infrared. Subsequently, we discuss the reliability challenges in cognitive sensing, taking into account hardware and algorithmic properties of AFE. The paper concludes with discussions on future research directions in this emerging field of cognitive sensors.

An autonomous system's perception engine must provide an accurate understanding of the environment for it to make decisions. Deep learning based object detection networks experience degradation in the performance and robustness for small and far away objects due to a reduction in object's feature map as we move to higher layers of the network. In this work, we propose a novel radar-guided spatial attention for RGB images to improve the perception quality of autonomous vehicles operating in a dynamic environment. In particular, our method improves the perception of small and long range objects, which are often not detected by the object detectors in RGB mode. The proposed method consists of two RGB object detectors, namely the Primary detector and a lightweight Secondary detector. The primary detector takes a full RGB image and generates primary detections. Next, the radar proposal framework creates regions of interest (ROIs) for object proposals by projecting the radar point cloud onto the 2D RGB image. These ROIs are cropped and fed to the secondary detector to generate secondary detections which are then fused with the primary detections via non-maximum suppression. This method helps in recovering the small objects by preserving the object's spatial features through an increase in their receptive field. We evaluate our fusion method on the challenging nuScenes dataset and show that our fusion method with SSD-lite as primary and secondary detector improves the baseline primary yolov3 detector's recall by 14 % while requiring three times fewer computational resources.

In this paper we present two novel complimentary methods namely the gradient analysis and the activation discrepancy analysis to analyze the perception failures occurring inside the DNN based object detectors. The gradient analysis localizes the nodes within the network that fail consistently in a scenario, thus creating a ‘signature’ of False Negatives (FNs). This method traces a set of False Negatives through the network and finds sections of the network that contribute to this set. The signatures show the location of the faulty nodes is sensitive to input conditions (such as darkness, glare etc.), network architecture, training hyperparameters, object class etc. Certain nodes of the network fail consistently throughout the training process thus implying that some False Negatives occur due to the global optimization nature of Stochastic Gradient Descent (SGD) based training. This analysis requires the knowledge of False Negatives and therefore can be used for post-hoc diagnostic analysis. On the other hand, the activation discrepancy analysis analyzes the discrepancy in forward activations of a DNN. This method can be conducted online and shows that the pattern of the activation discrepancy is sensitive to input conditions and detection recall.