Ayoosh Bansal

University of Illinois, Urbana-Champaign | UIUC · Department of Computer Science

Timing correctness is crucial in a multi-criticality real-time system, such as an autonomous driving system. It has been recently shown that these systems can be vulnerable to timing inference attacks, mainly due to their predictable behavioral patterns. Existing solutions like schedule randomization cannot protect against such attacks, often limit...

As Autonomous Vehicle (AV) development has progressed, concerns regarding the safety of passengers and agents in their environment have risen. Each real world traffic collision involving autonomously controlled vehicles has compounded this concern. Open source autonomous driving implementations show a software architecture with complex interdepende...

System auditing is a powerful tool that provides insight into the nature of suspicious events in computing systems, allowing machine operators to detect and subsequently investigate security incidents. While auditing has proven invaluable to the security of traditional computers, existing audit frameworks are rarely designed with consideration for...

Perception of obstacles remains a critical safety concern for autonomous vehicles. Real-world collisions have shown that the autonomy faults leading to fatal collisions originate from obstacle existence detection. Open source autonomous driving implementations show a perception pipeline with complex interdependent Deep Neural Networks. These networ...

System auditing is a powerful tool that provides insight into the nature of suspicious events in computing systems, allowing machine operators to detect and subsequently investigate security incidents. While auditing has proven invaluable to the security of traditional computers, existing audit frameworks are rarely designed with consideration for...

Object detection in state-of-the-art Autonomous Vehicles (AV) framework relies heavily on deep neural networks. Typically, these networks perform object detection uniformly on the entire camera LiDAR frames. However, this uniformity jeopardizes the safety of the AV by giving the same priority to all objects in the scenes regardless of their risk of...

This paper explores criticality-based real-time scheduling of neural-network-based machine inference pipelines in cyber-physical systems (CPS) to mitigate the effect of algorithmic priority inversion. We specifically focus on the perception subsystem, an important subsystem feeding other components (e.g., planning and control). In general, priori...

Commonly used metrics for evaluation of object detection systems (precision, recall, mAP) do not give complete information about their suitability of use in safety critical tasks, like obstacle detection for collision avoidance in Autonomous Vehicles (AV). This work introduces the Risk Ranked Recall ($R^3$) metrics for object detection systems. The...

Real-time systems have recently been shown to be vulnerable to timing inference attacks, mainly due to their predictable behavioral patterns. Existing solutions such as schedule randomization lack the ability to protect against such attacks, often limited by the system's real-time nature. This paper presents SchedGuard: a temporal protection framew...

The paper discusses algorithmic priority inversion in mission-critical machine inference pipelines used in modern neural-network-based cyber-physical applications, and develops a scheduling solution to mitigate its effect. In general, priority inversion occurs in real-time systems when computations that are of lower priority are performed together...

Hardware accelerators for neural networks have shown great promise for both performance and power. These accelerators are at their most efficient when optimized for a fixed functionality. But this inflexibility limits the longevity of the hardware itself as the underlying neural network algorithms and structures undergo improvements and changes. We...

Real-time and cyber-physical systems need to interact with and respond to their physical environment in a predictable time. While multicore platforms provide incredible computational power and throughput, they also introduce new sources of unpredictability. Large fluctuations in latency to access data shared between multiple cores is an important c...

This paper presents the evaluation of the memory subsystem of the Xilinx Ultrascale+ MPSoC. The characteristics of various memories in the system are evaluated using carefully instrumented micro-benchmarks. The impact of micro-architectural features like caches, prefetchers and cache-coherency are measured and discussed. The impact of multi-core co...

A typical real-time application is composed of periodic tasks with hard deadline constraints. It must also service a periodic tasks that are generated in response to external and internal events. In addition to application's timing constraints, it is important that the system never violates thermal constraint due to its increasingly adverse impact...