Lightweitght AI, 

[Arxiv] STAS: Spatio-Temporal Adaptive Computation Time for Spiking Transformers

• STAS is designed to dynamically adjust the number of tokens processed during inference in SNN-based vision transformers (ViTs), considering power consumption proportional to the number of tokens. It extends the applicability of Adaptive Computation Time (ACT), previously limited to RNNs and standard ViTs, to SNN-based ViTs by selectively discarding less informative spatial tokens, thereby improving efficiency.

[Arxiv] RT-SNN: Real-Time Scheduling Framework for Multi-Object Detection using Spiking Neural Networks

• The core principle of RT-SNN is to provide resource-efficient execution options for SNNs with varying execution times, ensuring timing guarantees while considering their impact on accuracy. RT-SNN employs a scheduling framework that dynamically selects the optimal execution option for accuracy while maintaining timing guarantees. Deployed on Spiking-YOLO, an SNN-based MOD model from ANN-to-SNN conversion, RT-SNN demonstrates effectiveness in meeting timing and accuracy requirements while improving energy efficiency. 

Secure AI, 

[IJCAI'25] BankTweak: Adversarial Attack against Multi-Object Trackers by Manipulating Feature Banks 

• BankTweak targets the feature extractor during the association phase, exposing vulnerabilities in the Hungarian matching method commonly used in feature-based MOT systems. By strategically injecting altered features into the feature banks without modifying object positions, BankTweak induces persistent ID switches, enhancing attack efficiency and robustness even after the attack ends.

[Arxiv] Timestep-Compressed Attack on Spiking Neural Networks through Timestep-Level Backpropagation

• The timestep-compressed attack (TCA) is a novel framework that addresses the high latency of adversarial attacks on Spiking Neural Networks (SNNs) by leveraging their unique operational properties. It introduces timestep-level backpropagation for early stopping and reuses the initial membrane potential to eliminate redundant computations, significantly accelerating attack generation.

and Physical AI. 

[RTSS'25] CF-DETR: Coarse-to-Fine Transformer for Real-Time Object Detection  

• CF-DETR leverages a coarse-to-fine detection Transformer architecture and a real-time scheduling framework to dynamically adjust inference based on object criticality. This approach guarantees timely detection of safety-critical objects while selectively improving overall accuracy, effectively managing the latency-accuracy trade-off in autonomous perception systems.

[EMSOFT'24] Batch-MOT: Batch-Enabled Real-Time Scheduling for Multi-Object Tracking Tasks  

• Batch-MOT is the first system designed to achieve both (G1) timing guarantees and (G2) accuracy maximization by leveraging batch execution, enabling multiple DNN executions within a single inference. It accomplishes G1 and G2 without significant runtime overhead by systematically exploiting runtime execution behaviors enabled by the adaptive framework.

[RTSS'22] RT-MOT: Confidence-Aware Real-Time Scheduling Framework for Multi-Object Tracking Tasks  

• RT-MOT is a novel system design for multi-object tracking (MOT) that addresses the dual requirements of timely execution and high tracking accuracy under limited computing resources. Utilizing a confidence-aware scheduling framework and predictive tracking accuracy estimation optimizes workload pair selection for detection and association, ensuring offline timing guarantees while maximizing real-time performance. 

An Application

[ITRC] Low Earth Orbit  (LEO) Satellite Systems

• We aim to develop a lightweight satellite reconnaissance system that simultaneously achieves real-time performance and high accuracy for multi-purpose LEO satellites.