We present a novel approach to monitor the Body in White (BiW), the fundamental metallic structure of a vehicle. Existing monitoring methods, including both wired and wireless sensor systems, face significant challenges due to integration complexity, weight considerations, material costs, and signal blockage within the metallic environment. To overcome these limitations, we introduce Arach-Net, an acoustic backscatter network that leverages the conductive properties of the BiW to propagate vibration signals for energy transfer and data communication. This system comprises battery-free tags that harvest energy from BiW vibrations and utilize a backscatter technique for efficient communication, thereby eliminating the need for external power sources and reducing the power consumption. We address key challenges such as power sufficiency for tag activation and sustained operation, and collision reduction in network communication, by designing an ultra-low power backscatter tag and a distributed slot allocation protocol. We implement ArachNet, and deploy 12 tags onto the BiW of an electric SUV car. The evaluation results show that the power consumption of the tag is 51.0 μW for uplink packet transmission, and 24.8 μW for downlink packet reception. With our network protocol, the slot utilization can be up to 81.2%.

MLIR (Multi-Level Intermediate Representation) compiler infrastructure has gained popularity in recent years to support the construction of many compilers. Instead of designing a new IR with a single abstraction for each domain, MLIR compiler infrastructure provides systematic passes to support a wide range of functionalities for benefiting multiple domains together and introduces dialects to support different levels of abstraction in MLIR. Due to its fundamental role in compiler community, ensuring its quality is very critical. In this work, we propose MLIRSmith, the first fuzzing technique for MLIR compiler infrastructure. MLIRSmith employs a two-phase strategy to generate valid and diverse MLIR programs, which first constructs diverse program templates guided by extended MLIR syntax rules and then generates valid MLIR programs through template instantiation guided by our designed context-sensitive grammar. After applying MLIRSmith to the latest revision of MLIR compiler infrastructure, we detected 53 previously unknown bugs, among which 49/38 have been confirmed/fixed by developers. We also transform the high-level programs generated by NNSmith (a high-level program generator for deep learning compilers) to MLIR programs for indirectly fuzzing MLIR compiler infrastructure. During the same testing time, MLIRSmith largely outperforms such an indirect technique by detecting 328.57% more bugs and covering 194.67%/225.87% more lines/branches in MLIR compiler infrastructure.