ISAX heterogeneous processors integrate cores that share a common base ISA, with certain cores offering extension ISAs (e.g., vector extension) to accelerate computation. ISAX balances performance and energy efficiency while facilitating the reuse of existing software ecosystems. RISC-V, which adopts the ISAX architecture, has gained extensive attention in both industry and academia. Binary translation via binary rewriting enables transparent ISAX heterogeneous computing by translating extension instructions when migrating a program to cores without extension support. However, current binary rewriting methods still struggle to achieve both high performance and correctness.

The openness and flexibility of the RISC-V instruction set architecture (ISA) have driven its widespread adoption in AIoT (Artificial Intelligence of Things) devices. However, existing operating systems (OSes) for real RISC-V hardware often suffer from poor application performance and large memory footprints. To address these issues, we propose an OS optimization scheme tailored for RISC-V in AIoT devices. First, we introduce an application-transparent performance enhancement mechanism that leverages both coarse- and fine-grained process management to improve the performance of applications, particularly in AI inference. Second, we design a low-memory-footprint software stack through theoretical analysis and careful trade-offs in the adoption of software components. Lastly, we develop a lightweight OS image construction strategy algorithm tailored for RISC-V in AIoT. Using our OS optimization scheme, we build PolyOS from scratch to reduce the OS image size, thereby further lowering memory footprint. Across four real RISC-V hardware platforms, PolyOS achieves up to a 142% overall system performance improvement and up to 5.90× speedup in AI inference applications compared to baseline OSes (Armbian, Nucleisys, etc.). It also significantly reduces the runtime memory footprint of the standard C library, OpenCV, QuickJS, and AI inference applications, while shrinking the OS image size to 1/3.14–1/23.48 of its baseline OS.

Collaborative inference accelerates DNN inference tasks of resource-limited devices (e.g., clients) by offloading model slices to resource-rich devices (e.g., servers). During the inference procedure, outputs of model slices are transmitted among devices, causing significant intermediate data transmission overhead and posing a risk of privacy leakage of the client’s input data. Quantization has been widely used in collaborative inference to enhance communication efficiency. However, traditional quantization cannot prevent data privacy leaks. Besides, perturbation-based privacy protection methods, such as adding Laplace noise to the intermediate data of collaborative inference, do not consider communication efficiency. In this paper, we introduce Layered Laplace Random Quantization to simultaneously achieve communication efficiency and data privacy protection in collaborative inference by compressing the intermediate data with Laplace quantization noise. We also propose stability training to recover the accuracy loss caused by our method. Evaluation results show that our method achieved an average inference latency speedup of 1.2x-1.3x for different DNN models compared with the baseline methods while achieving comparable data privacy protection and recoverable accuracy loss.

Shared libraries are widely used in software development to execute third-party functions. However, the size and complexity of shared libraries tend to increase with the need to support more features, resulting in bloated shared libraries. This leads to resource waste and security issues as a significant amount of generic functionality is included unnecessarily in most scenarios, especially in embedded systems. To address this issue, previous works attempt to debloat shared libraries through binary rewriting or recompilation. However, these works face a tradeoff between flexibility in usage (needs recompilation and runtime support) and the effectiveness of debloating (binary rewriting achieves insufficient file size reduction). We propose D-Linker, a tool that debloats shared libraries by reducing both code and data sections in link-time at the object level without recompilation. Our key insight is that object-level shared library debloating is especially suitable for embedded systems because it strikes a balance of flexibility and efficiency. D-Linker identifies the required ELF object files of the shared libraries in an application and relinks them to produce a debloated shared library with better-debloating effectiveness by avoiding the data reference analysis. Our approach achieves over 70% of gadgets reduction as a security benefit and an average size reduction of 49.6% for a stripped libc of coreutils. The results also indicate that D-Linker improves debloating effectiveness by approximately 30% compared to binary-level shared library debloating and incurs a 5% decrease in code gadgets reduction compared to source-code-level shared library debloating.

Abstract The command line interface is a crucial way of interacting with Linux, many programs such as ls, pwd and netstat are used on it and it is also the primary way to access a server remotely. However, the command line interface is not user friendly and thus it is difficult to use; there are many programs and users do not know which one is appropriate for finishing their task. To help users find useful commands efficiently, the authors propose FindCmd that retrieves commands based on the local data and user familiarity with commands. Then the local command data are collected including user manual such as man , info and strings extracted from the binary ELF (executable and linkable format) file. Based on the characteristics of local data, an enhanced command retrieval framework is proposed. In addition, the authors marginally decreased the priority of familiar commands when retrieving commands since users tend to use command retrieval tool to find an unfamiliar command. To the best of our knowledge, this is the first local tool for personalised command retrieval. In the evaluation section, the authors compare FindCmd with retrieval tools apropos and howdoi ; our experimental results show that FindCmd outperforms the other two tools in retrieving commands. In addition, the experiments demonstrate the effectiveness of personalised search of FindCmd.

Hypervisors are becoming a widespread virtualisation layer in current computer systems. Recent successful attacks against hypervisors indicate that they face the similar integrity threats as traditional operating systems. Current approaches that secure hypervisors mainly focus on code or control‐data integrity, without paying attention to non‐control data integrity. In this study the authors construct attacks that target hypervisor non‐control data to demonstrate which types of data within the Xen hypervisor are critical to system security. It shows privilege, resource utilisation and security policy related data are vulnerable to return‐oriented programming or DMA attacks. By modifying their values from one to another, the whole system's performance will be affected. By discussing current approaches that secure hypervisors, which are not suitable for non‐control data, the work is to motivate new innovation in this area to protect them.