Related papers: Sound, Precise, and Fast Abstract Interpretation w…

Extended Berkeley Packet Filter (BPF) has emerged as a powerful method to extend packet-processing functionality in the Linux operating system. BPF allows users to write code in high-level languages (like C or Rust) and execute them at…

Extended Berkeley Packet Filter (eBPF) is a runtime that enables users to load programs into the operating system (OS) kernel, like Linux or Windows, and execute them safely and efficiently at designated kernel hooks. Each program passes…

Many modern solvers and program analyzers rely on non-monotone reasoning (e.g. negation-as-failure, speculative updates, backtracking) for which classical monotone fixed-point methods do not apply. The general problem of finding the fixed…

Training Neural Networks (NNs) to serve as Barrier Functions (BFs) is a popular way to improve the safety of autonomous dynamical systems. Despite significant practical success, these methods are not generally guaranteed to produce true BFs…

The Transformer is the foundational building block of modern AI, yet offers no principled handling of \emph{uncertainty}, which is prevalent in real applications: cold-start tokens with sparse histories in sequential recommendation,…

The ability to modify and extend an operating system is an important feature for improving a system's security, reliability, and performance. The extended Berkeley Packet Filters (eBPF) ecosystem has emerged as the standard mechanism for…

As large language models (LLMs) move from research to production, understanding how inference engines behave in real time has become both essential and elusive. Unlike general-purpose engines such as ONNX Runtime, today's LLM inference…

The Fast Fourier Transform (FFT), as a core computation in a wide range of scientific applications, is increasingly threatened by reliability issues. In this paper, we introduce TurboFFT, a high-performance FFT implementation equipped with…

We completely describe a new domain for abstract interpretation of numerical programs. Fixpoint iteration in this domain is proved to converge to finite precise invariants for (at least) the class of stable linear recursive filters of any…

This paper addresses the challenging problem of parameter estimation in bilinear systems under colored noise. A novel approach, termed B-PF-RLS, is proposed, combining a particle filter (PF) with a recursive least squares (RLS) estimator.…

We show that abstract interpretation-based static program analysis can be made efficient and precise enough to formally verify a class of properties for a family of large programs with few or no false alarms. This is achieved by refinement…

With rapid improvements in NVM storage devices, the performance bottleneck is gradually shifting to the network, thus giving rise to the notion of "data movement wall". To reduce the amount of data movement over the network, researchers…

The Linux kernel extensively uses the Berkeley Packet Filter (BPF) to allow user-written BPF applications to execute in the kernel space. The BPF employs a verifier to check the security of user-supplied BPF code statically. Recent attacks…

Inaccuracies in conventional dependency-tracking methods frequently undermine the security and integrity of modern software supply chains. This paper introduces a kernel-level framework leveraging extended Berkeley Packet Filter (eBPF) to…

Binary-level pointer analysis can be of use in symbolic execution, testing, verification, and decompilation of software binaries. In various such contexts, it is crucial that the result is trustworthy, i.e., it can be formally established…

As the volume of data that needs to be processed continues to increase, we also see renewed interests in near-data processing in the form of computational storage, with eBPF (extended Berkeley Packet Filter) being proposed as a vehicle for…

The data analysis of space-based gravitational wave detectors like Taiji faces significant challenges from non-stationary noise, which compromises the efficacy of traditional frequency-domain analysis. This work proposes a unified framework…

Static analysis by abstract interpretation aims at automatically proving properties of computer programs. To do this, an over-approximation of program semantics, defined as the least fixpoint of a system of semantic equations, must be…

Bootstrap particle filter (BPF) is the corner stone of many popular algorithms used for solving inference problems involving time series that are observed through noisy measurements in a non-linear and non-Gaussian context. The long term…

Binarized Neural Networks (BNN) offer efficient implementations for machine learning tasks and facilitate Privacy-Preserving Machine Learning (PPML) by simplifying operations with binary values. Nevertheless, challenges persist in terms of…