arXiv subjects

Ernest Ng

Publications and source records attributed to Ernest Ng.

3 recordsLinked to original sources

arXiv2026

Specifying Hardware Communication as Programs

To test and debug hardware modules, it is common to write two programs: a driver, which translates high-level transactions into interactions on the module's input and output signals, and a monitor, which analyzes a signal-level execution trace and recognizes a transaction. These two programs are commonly implemented separately for each hardware protocol, but this separation entails manual effort and risks inconsistencies. We advocate an alternative approach. We present a DSL in which users specify hardware communication protocols as succinct imperative programs. Crucially, the same specification can be used to both drive designs and monitor transactions. We present the design of a tool, which given a specification in our DSL and a waveform, automatically infers a transaction-level trace consistent with the waveform. We discuss plans to evaluate our DSL on real-world interconnects such as Wishbone and AXI-Stream.

cs.PL↗

arXiv2024

Mica: Automated Differential Testing for OCaml Modules

Suppose we are given two OCaml modules implementing the same signature. How do we check that they are observationally equivalent -- that is, that they behave the same on all inputs? One established technique is to use a property-based testing (PBT) tool such as QuickCheck. Currently, however, this can require significant amounts of boilerplate code and ad-hoc test harnesses. To address this issue, we present Mica, an automated tool for testing observational equivalence of OCaml modules. Mica is implemented as a PPX compiler extension, allowing users to supply minimal annotations to a module signature. These annotations guide Mica to automatically derive specialized PBT code that checks observational equivalence. We discuss the design of Mica and demonstrate its efficacy as a testing tool on various modules taken from real-world OCaml libraries.

cs.PL↗

arXiv2020

Self-Evolving Adaptive Learning for Personalized Education

Primary and secondary education is a crucial stage to build a strong foundation before diving deep into specialised subjects in colleges and universities. To excel in the current education system, students are required to have a deep understanding of knowledge according to standardized curriculums and syllabus, and exam-related problem solving skills. In current school settings, this learning normally occurs in large classes of 30-40 students per class. Such a ``one size fits all'' approach may not be effective, as different students proceed on their learning in different ways and pace. To address this problem, we propose the Self-Evolving Adaptive Learning (SEAL) system for personalized education at scale.

physics.ed-ph↗