The growing complexity and size of integrated circuits has made functional verification a huge challenge. As the control center of integrated circuit hardware design, any design errors in the Instruction Pipeline Control Unit (IPCU) will put the enti...
Accurate branch prediction is crucial for achieving high instruction throughput and minimizing control hazards in modern pipelines. This paper presents a novel LXOR (Local eXclusive-OR) branch predictor, which enhances prediction accuracy while reduc...
Recruitment automation increasingly relies on Large Language Models (LLMs) for extracting structured information from unstructured CVs and job postings. However, production data often arrive as heterogeneous, privacy-sensitive PDFs, limiting reproduc...
Pipelines, in Reduced Instruction Set Computer (RISC) microprocessors, are expected to provide increased throughputs in most cases. However, there are a few instructions, and therefore entire assembly language codes, that execute faster and hazard-fr...
Extracting features from sensing data on edge devices is a challenging application for which deep neural networks (DNN) have shown promising results. Unfortunately, the general micro-controller-class processors which are widely used in sensing system...
Multithreading is widely used in microcontroller unit (MCU) chips. Multithreaded hardware is composed of multiple identical single threads and provides instructions to different threads. Using the concept of thread-level parallelism (TLP), pauses are...
To ensure the timely execution of hard real-time applications, scheduling analysis techniques must consider safe upper bounds on the possible execution durations of tasks or runnables, which are referred to as Worst-Case Execution Times (WCET). Bound...
Application-specific instruction set processors (ASIPs) often employ specialized hardware to improve performance, but this introduces complexity in resource management and programming. Existing compiler solutions, including LLVM’s default sched...
Personalized instruction remains a major bottleneck in higher education, especially in large classes where timely, individualized feedback is difficult to achieve. Existing automation typically relies on rigid rule-based pipelines or computationally...
The widespread adoption of fifth-generation Reduced Instruction Set Computing (RISC-V) processors in embedded systems has driven advancements in domestic processor design. However, research on processor performance optimization methods predominantly...
As the key module of programmable switches or the SmartNIC card, the packet processing pipeline undertakes the task of packet forwarding and processing. However, the current pipeline for the FPGA-based SmartNIC is inflexible, and the related reconfig...
Loop unrolling can provide more instruction-level parallelism opportunities for code and enables a greater range of instruction pipeline scheduling. In high-performance very-long-instruction-word (VLIW) digital signal processors (DSPs), there are spe...
With the development of 5G and network technology, the usage of IoT devices has become popular. Because most of these IoT devices can be controlled by an adversary away from the administrator, several security issues such as firmware dumping can aris...
Large language models (LLMs) are increasingly used as rubric-guided graders for short-answer exams, but their decisions can be unstable across prompts and vulnerable to answer-side prompt injection. In this paper, we study SteadyEval, a guardrailed e...
The growing demand for high-performance and energy-efficient processing in edge-oriented Systems-on-Chip is driving the adoption of dedicated integrated circuits that accelerate computationally intensive workloads. To minimize area and performance ov...
Efficient multimodal instruction tuning requires reducing both training data volume and computational cost without substantially sacrificing downstream performance. Existing data selection methods can remove redundancy but cannot repair imperfect sup...
This paper addresses text simplification task for Kazakh, a morphologically rich, low-resource language, by introducing KazSim, an instruction-tuned model built on multilingual large language models (LLMs). First, we develop a heuristic pipeline to i...
In very large-scale integration circuit (VLSI) systems, microcontrollers are often implanted to manage the whole system to complete the given computing tasks. They play an essential part as regulators, which should allocate resources steadily and iss...
Background: Conventional machine learning courses are usually designed for academic learners, instead of working professionals. This study addresses this gap by proposing a new instructional framework that builds practical computational thinking skil...
China–Myanmar oil and gas pipelines in Southwest China guarantee the energy security of China. Due to poor geographical circumstances, the safety of pipelines is seriously threatened by natural disasters. Therefore, there is a crucial, practica...
Complex scalar kernels often contain instruction fragments whose computation is simple but whose operand demand exceeds the traditional two-input–one-output custom-instruction model. This operand interface bottleneck limits instruction merging...
The fetched instructions would have data dependency with in-flight ones in the pipeline execution of a processor, so the dependency prevents the processor from executing the incoming instructions for guaranteeing the program’s correctness. The...
Addressing the challenges of dense conceptual content and intricate knowledge relations in computer science textbooks, where traditional pipeline-based information extraction suffers from error propagation and semantic decoupling, this paper proposes...
Embedded processors are widely used in various systems working on different tasks with different workloads. A more complex micro-architecture leads to better peak performance and worse power consumption. Shutting down the units designed for performan...
Mathematical expression evaluation is a critical component in high-performance software systems. Traditional state-dependent algorithms resist Single Instruction, Multiple Data vectorization, while managed runtime environments introduce Garbage Colle...
Instruction Set Architecture (ISA) extensions, particularly scalar cryptography extensions (Zk), combine the performance advantages of hardware with the adaptability of software, enabling the direct and efficient execution of cryptographic functions...
Computer systems research faces significant challenges in reproducibility because of toolchain fragmentation and the rapid evolution of the RISC-V ecosystem. Many research artifacts stay as ‘digital tombstones’ because they lack stable bu...
Exponential advances in computational power have fueled advances in many disciplines, and biology is no exception. High-Performance Computing (HPC) is gaining traction as one of the essential tools in scientific research. Further advances to exascale...
Based on branch prediction, value prediction has emerged as a solution for problems caused by true data dependencies in pipelined processors. While branch predictors have binary outcomes (taken/not taken), value predictors face a challenging task as...
The identification of high risk regions is an important aim of risk-based inspections (RBIs) in pipeline networks. As the most vital part of risk-based inspections, risk assessment makes a significant contribution to achieving this aim. Accurate asse...
Reliable evaluation of large language models (LLMs) for educational use requires benchmarks that reflect exam constraints, instructor grading practices, and the operational consequences of thresholded decisions. This paper introduces ExamQ-Gen, an in...
Extreme summarization generates very short summaries, typically a single sentence, answering the question “What is the document about?”. Although large language models perform well in text generation, fine-tuning them for summarization of...
Large language models (LLMs) increasingly require robust evaluation under realistic instruction-following conditions, particularly for fine-tuned task-specific adapters operating in multilingual environments. This study proposes a scenario-adaptive e...
Text-to-SQL research has concentrated on English, while prior work on Arabic, Japanese, and Vietnamese has largely relied on grammar-constrained parsers or zero-shot prompting, with recent work beginning to explore trained LLM-based alternatives. We...
24 November 2022
Background: High-quality oligonucleotides for molecular amplification and detection procedures of diverse target sequences depend on sequence homology. Processing input sequences and identifying homogeneous regions in alignments can be carried out by...
In air traffic control (ATC), speech communication with radio transmission is the primary way to exchange information between the controller and aircrew. A wealth of contextual situational dynamics is embedded implicitly; thus, understanding the spok...
Low-resource languages remain underserved by contemporary large language models (LLMs) because they lack sizable corpora, bespoke preprocessing tools, and the computing budgets assumed by mainstream alignment pipelines. Focusing on Kazakh, we present...
Large Language Model (LLM)-based agents automate complex tasks by integrating external tools such as web browsers, e-mail clients, file readers, and APIs, but this same integration exposes them to indirect prompt injection (IPI) attacks, in which mal...
The manual classification of diplomatic documents by security sensitivity level is labor-intensive and inconsistent at scale. This study proposes SACHN-DeBERTa-v3-Large, a hybrid classification architecture integrating a DeBERTa-v3-Large backbone wit...
Most adaptive learning systems personalize around content sequencing and difficulty adjustment rather than transforming instructional material within the lesson itself. This paper presents the STREAM (Semantic Transformation and Real-Time Educational...
Real-time target detection for hyperspectral images (HSI) has received considerable interest in recent years. However, owing to enormous data volume provided by HSI, detection algorithms are generally computationally complex, thus developing rapid pr...
The increasing ability of Large Language Models (LLMs) to generate fluent and coherent text has heightened the need for resources to analyze and detect synthetic content, particularly in Spanish, where the scarcity of datasets hinders the development...
This paper presents a unified technical survey of Large Language Models (LLMs), connecting three layers of the modelling pipeline that existing surveys address in isolation: internal architecture, distributed training optimisation, and downstream ada...
Rice crop residue burning (RCRB) in India constitutes a major annual environmental crisis, contributing significantly to regional air pollution, greenhouse gas emissions, and public health deterioration across the Indo-Gangetic Plain. Despite growing...
Software-defined networking (SDN) separates the control plane and the data plane, which provides network applications with global network topology and the flexibility to customize packet forwarding rules. SDN has a wide range of innovative applicatio...
Knitting, a cornerstone of textile manufacturing, is uniquely challenging to automate, particularly in terms of converting fabric designs into precise, machine-readable instructions. This research bridges the gap between textile production and roboti...
The shrinking of technology nodes allows higher performance, but susceptibility to soft errors increases. The protection has been implemented mainly by lockstep or hardened process techniques, which results in a lower frequency, a larger area, and hi...
Code instrumentation enables the observability of an embedded software system during its execution. A usage example of code instrumentation is the estimation of “worst-case execution time” using hybrid analysis. This analysis combines sta...
Background/Objectives: Large Language Models (LLMs) like ChatGPT, LLAMA, and Mistral are widely used for automating tasks such as content creation and data analysis. However, due to their training on publicly available internet data, they may inherit...
This paper presents a comparative benchmarking study evaluating the regulatory comprehension of four open-source large language models, TinyLlama-1.1B-Chat, Gemma-2B-Instruct, Llama-3.1-8B-Instruct, and Mistral-7B-Instruct-v0.3, on the EU Artificial...
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