Modern autonomous driving has widely used deep learning to process point cloud data. This application is widely deployed on embedded edge computing devices and has high security requirements. We found that backdoor attacks can pose an extremely serious threat to point cloud deep learning systems, but this attack method has not been explored in point cloud deep learning tasks. In this paper, we propose a physically implementable backdoor attack method for the point cloud deep learning model. This method can achieve good performance in the attack effect and physical realization, evaluating by preliminary experiments.
Modern embedded systems need to cater for several needs depending upon the application domain in which they are deployed. For example, mixed-critically needs to be considered for real-time and safety-critical systems and energy for battery-operated systems. At the same time, many of these systems demand for their reliability and security as well. With electronic systems being used for increasingly varying type of applications, novel challenges have emerged. For example, with the use of embedded systems in increasingly complex applications that execute tasks with varying priorities, mixed-criticality systems present unique challenges to designing reliable systems. The large design space involved in implementing cross-layer reliability in heterogeneous systems, particularly for mixed-critical systems, poses new research problems. Further, malicious security attacks on these systems pose additional extraordinary challenges in the system design. In this paper, we cover both the industry and academia perspectives of the challenges posed by these emergent aspects of system design towards designing high-performance, energy-efficient, reliable and/or secure embedded systems. We also provide our views on paths forward.
The current trend of transforming static embedded systems into open platforms (in which several software providers are able to directly load their software) drives the need to design and implement modular embedded software. Additionally, a myriad of embedded devices are expected to operate and provide services for years, or even decades, while remaining correct and secure at all times. Therefore, one of the emerging challenges for highly adaptive embedded computing platforms is to offer dynamic software composition at runtime and internal device housekeeping, which in conjunction improve device maintainability. We propose a hardware/software co-designed technique to improve both security and maintainability in modular embedded systems.
This work presents a plan for investigating transistor aging and degradation in cyber physical systems under attack. The authors discuss the importance of such concerns, particularly with regard to negative bias temperature instability, and an approach to demonstrate the adverse effects of a cyber-attack resulting in premature aging of transistors within a system's digital controllers.
Gigantic rates of data production in the era of Big Data, Internet of Thing (IoT), and Smart Cyber Physical Systems (CPS) pose incessantly escalating demands for massive data processing, storage, and transmission while continuously interacting with the physical world using edge sensors and actuators. For IoT systems, there is now a strong trend to move the intelligence from the cloud to the edge or the extreme edge (known as TinyML). Yet, this shift to edge AI systems requires to design powerful machine learning systems under very strict resource constraints. This poses a difficult design task that needs to take the complete system stack from machine learning algorithm, to model optimization and compression, to software implementation, to hardware platform and ML accelerator design into account. This paper discusses the open research challenges to achieve such a holistic Design Space Exploration for a HW/SW Co-design for Edge AI Systems and discusses the current state with three currently developed flows: one design flow for systems with tightly-coupled accelerator architectures based on RISC-V, one approach using loosely-coupled, application-specific accelerators as well as one framework that integrates software and hardware optimization techniques to built efficient Deep Neural Network (DNN) systems.
