Biodiversity loss is a critical planetary boundary, yet its connection to computing remains largely unexamined. Prior sustainability efforts in computing have focused on carbon and water, overlooking biodiversity due to the lack of appropriate metrics and modeling frameworks. This paper presents the first end-to-end analysis of biodiversity impact from computing systems. We introduce two new metrics--Embodied Biodiversity Index (EBI) and Operational Biodiversity Index (OBI)--to quantify biodiversity impact across the lifecycle, and present FABRIC, a modeling framework that links computing workloads to biodiversity impacts. Our evaluation highlights the need to consider biodiversity alongside carbon and water in sustainable computing design and optimization. The code is available at https://github.com/TianyaoShi/FABRIC.
Stacy A. Doore, Michelle Trim, Joycelyn Streator
et al.
Teaching applied ethics in computer science has shifted from a perspective of teaching about professional codes of conduct and an emphasis on risk management towards a broader understanding of the impacts of computing on humanity and the environment and the principles and practices of responsible computing. One of the primary shifts in the approach to teaching computing ethics comes from research in the social sciences and humanities. This position is grounded in the idea that all computing artifacts, projects, tools, and products are situated within a set of ideas, attitudes, goals, and cultural norms. This means that all computing endeavors have embedded within them a set of values. To teach responsible computing always requires us to first recognize that computing happens in a context that is shaped by cultural values, including our own professional culture and values. The purpose of this paper is to highlight current scholarship, principles, and practices in the teaching of responsible computing in undergraduate computer science settings. The paper is organized around four primary sections: 1) a high-level rationale for the adoption of different pedagogical approaches based on program context and course learning goals, 2) a brief survey of responsible computing pedagogical approaches; 3) illustrative examples of how topics within the CS 2023 Social, Ethical, and Professional (SEP) knowledge area can be implemented and assessed across the broad spectrum of undergraduate computing courses; and 4) links to examples of current best practices, tools, and resources for faculty to build responsible computing teaching into their specific instructional settings and CS2023 knowledge areas.
Large language models (LLMs) have rapidly gained popularity and are being embedded into professional applications due to their capabilities in generating human-like content. However, unquestioned reliance on their outputs and recommendations can be problematic as LLMs can reinforce societal biases and stereotypes. This study investigates how LLMs, specifically OpenAI's GPT-4 and Microsoft Copilot, can reinforce gender and racial stereotypes within the software engineering (SE) profession through both textual and graphical outputs. We used each LLM to generate 300 profiles, consisting of 100 gender-based and 50 gender-neutral profiles, for a recruitment scenario in SE roles. Recommendations were generated for each profile and evaluated against the job requirements for four distinct SE positions. Each LLM was asked to select the top 5 candidates and subsequently the best candidate for each role. Each LLM was also asked to generate images for the top 5 candidates, providing a dataset for analysing potential biases in both text-based selections and visual representations. Our analysis reveals that both models preferred male and Caucasian profiles, particularly for senior roles, and favoured images featuring traits such as lighter skin tones, slimmer body types, and younger appearances. These findings highlight underlying societal biases influence the outputs of LLMs, contributing to narrow, exclusionary stereotypes that can further limit diversity and perpetuate inequities in the SE field. As LLMs are increasingly adopted within SE research and professional practices, awareness of these biases is crucial to prevent the reinforcement of discriminatory norms and to ensure that AI tools are leveraged to promote an inclusive and equitable engineering culture rather than hinder it.
Large language models (LLMs) have revolutionized AI applications, yet their enormous computational demands severely limit deployment and real-time performance. Quantization methods can help reduce computational costs, however, attaining the extreme efficiency associated with ultra-low-bit quantized LLMs at arbitrary precision presents challenges on GPUs. This is primarily due to the limited support for GPU Tensor Cores, inefficient memory management, and inflexible kernel optimizations. To tackle these challenges, we propose a comprehensive acceleration scheme for arbitrary precision LLMs, namely APT-LLM. Firstly, we introduce a novel data format, bipolar-INT, which allows for efficient and lossless conversion with signed INT, while also being more conducive to parallel computation. We also develop a matrix multiplication (MatMul) method allowing for arbitrary precision by dismantling and reassembling matrices at the bit level. This method provides flexible precision and optimizes the utilization of GPU Tensor Cores. In addition, we propose a memory management system focused on data recovery, which strategically employs fast shared memory to substantially increase kernel execution speed and reduce memory access latency. Finally, we develop a kernel mapping method that dynamically selects the optimal configurable hyperparameters of kernels for varying matrix sizes, enabling optimal performance across different LLM architectures and precision settings. In LLM inference, APT-LLM achieves up to a 3.99$\times$ speedup compared to FP16 baselines and a 2.16$\times$ speedup over NVIDIA CUTLASS INT4 acceleration on RTX 3090. On RTX 4090 and H800, APT-LLM achieves up to 2.44$\times$ speedup over FP16 and 1.65$\times$ speedup over CUTLASS integer baselines.
Explainable artificial intelligence (XAI) enhances AI system transparency by framing interpretability as an optimization problem. However, this approach often necessitates numerous iterations of computationally intensive operations, limiting its applicability in real-time scenarios. While recent research has focused on XAI hardware acceleration on FPGAs and TPU, these methods do not fully address energy efficiency in real-time settings. To address this limitation, we propose XAIedge, a novel framework that leverages approximate computing techniques into XAI algorithms, including integrated gradients, model distillation, and Shapley analysis. XAIedge translates these algorithms into approximate matrix computations and exploits the synergy between convolution, Fourier transform, and approximate computing paradigms. This approach enables efficient hardware acceleration on TPU-based edge devices, facilitating faster real-time outcome interpretations. Our comprehensive evaluation demonstrates that XAIedge achieves a $2\times$ improvement in energy efficiency compared to existing accurate XAI hardware acceleration techniques while maintaining comparable accuracy. These results highlight the potential of XAIedge to significantly advance the deployment of explainable AI in energy-constrained real-time applications.
Alireza Khadem, Daichi Fujiki, Hilbert Chen
et al.
In-cache computing technology transforms existing caches into long-vector compute units and offers low-cost alternatives to building expensive vector engines for mobile CPUs. Unfortunately, existing long-vector Instruction Set Architecture (ISA) extensions, such as RISC-V Vector Extension (RVV) and Arm Scalable Vector Extension (SVE), provide only one-dimensional strided and random memory accesses. While this is sufficient for typical vector engines, it fails to effectively utilize the large Single Instruction, Multiple Data (SIMD) widths of in-cache vector engines. This is because mobile data-parallel kernels expose limited parallelism across a single dimension. Based on our analysis of mobile vector kernels, we introduce a long-vector Multi-dimensional Vector ISA Extension (MVE) for mobile in-cache computing. MVE achieves high SIMD resource utilization and enables flexible programming by abstracting cache geometry and data layout. The proposed ISA features multi-dimensional strided and random memory accesses and efficient dimension-level masked execution to encode parallelism across multiple dimensions. Using a wide range of data-parallel mobile workloads, we demonstrate that MVE offers significant performance and energy reduction benefits of 2.9x and 8.8x, on average, compared to the SIMD units of a commercial mobile processor, at an area overhead of 3.6%.
This paper investigates the utilization of Quantum Computing and Neuromorphic Computing for Safe, Reliable, and Explainable Multi_Agent Reinforcement Learning (MARL) in the context of optimal control in autonomous robotics. The objective was to address the challenges of optimizing the behavior of autonomous agents while ensuring safety, reliability, and explainability. Quantum Computing techniques, including Quantum Approximate Optimization Algorithm (QAOA), were employed to efficiently explore large solution spaces and find approximate solutions to complex MARL problems. Neuromorphic Computing, inspired by the architecture of the human brain, provided parallel and distributed processing capabilities, which were leveraged to develop intelligent and adaptive systems. The combination of these technologies held the potential to enhance the safety, reliability, and explainability of MARL in autonomous robotics. This research contributed to the advancement of autonomous robotics by exploring cutting-edge technologies and their applications in multi-agent systems. Codes and data are available.
Ngoc‐Tuan Do, Van‐Phuc Hoang, Van Sang Doan
et al.
Abstract In modern embedded systems, security issues including side‐channel attacks (SCAs) are becoming of paramount importance since the embedded devices are ubiquitous in many categories of consumer electronics. Recently, deep learning (DL) has been introduced as a new promising approach for profiled and non‐profiled SCAs. This paper proposes and evaluates the applications of different DL techniques including the Convolutional Neural Network and the multilayer perceptron models for non‐profiled attacks on the AES‐128 encryption implementation. Especially, the proposed network is fine‐tuned with different number of hidden layers, labelling techniques and activation functions. Along with the designed models, a dataset reconstruction and labelling technique for the proposed model has also been performed for solving the high dimension data and imbalanced dataset problem. As a result, the DL based SCA with our reconstructed dataset for different targets of ASCAD, RISC‐V microcontroller, and ChipWhisperer boards has achieved a higher performance of non‐profiled attacks. Specifically, necessary investigations to evaluate the efficiency of the proposed techniques against different SCA countermeasures, such as masking and hiding, have been performed. In addition, the effect of the activation function on the proposed DL models was investigated. The experimental results have clarified that the exponential linear unit function is better than the rectified linear unit in fighting against noise generation‐based hiding countermeasure.
Alberto Rodríguez Rodríguez, Wilter Leonel Solórzano Álava, Dunia Lisbeth Domínguez Gálvez
et al.
La investigación científica requiere de la búsqueda y clasificación de la información para la elaboración de nuevos conocimientos. En la actualidad la mayoría de las fuentes de información se encuentran digitalizadas y son posible acceder desde Internet. Sin embargo, el nuevo escenario digitalizado implementa un reto para los investigadores en función de aprovechar el conocimiento existente. Problemas de esta naturaleza han sido abordados en la literatura científica mediante el uso de herramientas infotecnológicas. La presente investigación tiene como objetivo realizar un estudio sobre las herramientas infotecnológicas aplicadas a la metodología de investigación científica educativa, por lo que constituye un resultado del proyecto de investigación en ejecución en la carrera de Educación de la Universidad Estatal del Sur de Manabí, titulado: - Perfeccionamiento de las prácticas pedagógicas en las instituciones educativas de la zona sur de Manabí. Se realiza un análisis bibliográfico donde se caracterizan el conjunto de herramientas identificadas. Se aplicó una encuentra estructurada al grupo de profesores de la carrera de Educación en la Universidad Estatal del Sur de Manabí a través de la cual fue posible conocer el grado de implementación de las herramientas identificadas. Se pudo, además, identificar las principales causas que no han propiciado su generalización en las investigaciones científicas educativas.
Wan Aina Syahirah Wan Abdullah, Mardhiah Wahab, Yang Zeyu
et al.
Malaysia possesses significant potential for solar power generation due to its tropical weather and high levels of solar irradiance. This climate condition implies that solar technology, such as photovoltaic (PV) systems enable to generate more electricity per unit area, making it more economically viable for businesses and households’ applications. As Malaysia pledge to support the Net Zero Emissions (NZE) by 2050 Scenario, pre and continuous evaluation on the opportunities and challenges confronting solar technology penetration as a clean and affordable energy is of significant. This work aims to perform preliminary assessment of large scale of solar technology in Malaysia via integrated SWOT (Strengths, Weaknesses, Opportunities, and Threats) and PESTLE (the Political, Economic, Social, Technical, Legal, and Environmental) approaches, combine with analytic hierarchy process (AHP) method. It can be concluded that solar energy is one of the alternative energy sources that should be developed more in the future in terms of technology to ensure clean energy can be promoted. In AHP analysis, the economic aspect has shown the highest priority. Cost of investment and operation exhibited a huge factor that may hinder the large-scale solar project in Malaysia. The semi-empirical result of this paper presents a reality check on the solar technology feasibility in Malaysia while formulate a decision-making framework for addressing clean energy technology.
Chemical engineering, Computer engineering. Computer hardware
The modeling of multi-scale and multi-physics complex systems typically involves the use of scientific software that can optimally leverage extreme scale computing. Despite major developments in recent years, these simulations continue to be computationally intensive and time consuming. Here we explore the use of AI to accelerate the modeling of complex systems at a fraction of the computational cost of classical methods, and present the first application of physics informed neural operators (NOs) (PINOs) to model 2D incompressible magnetohydrodynamics (MHD) simulations. Our AI models incorporate tensor Fourier NOs as their backbone, which we implemented with the TensorLY package. Our results indicate that PINOs can accurately capture the physics of MHD simulations that describe laminar flows with Reynolds numbers $\mathrm{Re}\leqslant250$ . We also explore the applicability of our AI surrogates for turbulent flows, and discuss a variety of methodologies that may be incorporated in future work to create AI models that provide a computationally efficient and high fidelity description of MHD simulations for a broad range of Reynolds numbers. The scientific software developed in this project is released with this manuscript.
Machine learning (ML) components are being added to more and more critical and impactful software systems, but the software development process of real-world production systems from prototyped ML models remains challenging with additional complexity and interdisciplinary collaboration challenges. This poses difficulties in using traditional software lifecycle models such as waterfall, spiral, or agile models when building ML-enabled systems. In this research, we apply a Systems Engineering lens to investigate the use of V-Model in addressing the interdisciplinary collaboration challenges when building ML-enabled systems. By interviewing practitioners from software companies, we established a set of 8 propositions for using V-Model to manage interdisciplinary collaborations when building products with ML components. Based on the propositions, we found that despite requiring additional efforts, the characteristics of V-Model align effectively with several collaboration challenges encountered by practitioners when building ML-enabled systems. We recommend future research to investigate new process models, frameworks and tools that leverage the characteristics of V-Model such as the system decomposition, clear system boundary, and consistency of Validation & Verification (V&V) for building ML-enabled systems.
The amount of data processed in the cloud, the development of Internet-of-Things (IoT) applications, and growing data privacy concerns force the transition from cloud-based to edge-based processing. Limited energy and computational resources on edge push the transition from traditional von Neumann architectures to In-memory Computing (IMC), especially for machine learning and neural network applications. Network compression techniques are applied to implement a neural network on limited hardware resources. Quantization is one of the most efficient network compression techniques allowing to reduce the memory footprint, latency, and energy consumption. This paper provides a comprehensive review of IMC-based Quantized Neural Networks (QNN) and links software-based quantization approaches to IMC hardware implementation. Moreover, open challenges, QNN design requirements, recommendations, and perspectives along with an IMC-based QNN hardware roadmap are provided.
The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024 addresses maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicles (USV). Three challenges categories are considered: (i) UAV-based Maritime Object Tracking with Re-identification, (ii) USV-based Maritime Obstacle Segmentation and Detection, (iii) USV-based Maritime Boat Tracking. The USV-based Maritime Obstacle Segmentation and Detection features three sub-challenges, including a new embedded challenge addressing efficicent inference on real-world embedded devices. This report offers a comprehensive overview of the findings from the challenges. We provide both statistical and qualitative analyses, evaluating trends from over 195 submissions. All datasets, evaluation code, and the leaderboard are available to the public at https://macvi.org/workshop/macvi24.
HUANG Yufan, PENG Nuoheng, LIN Yan, FAN Jiancun, ZHANG Yijin, YU Yanqiu
To address the scarcity of spectrum resources in Internet of Vehicles(IoV), a novel multi-agent dynamic spectrum allocation solution based on Soft Actor-Critic(SAC) reinforcement learning is proposed.The solution aims to maximize the total channel capacity and the success rate of payload delivery.To achieve this goal, a spectrum resource allocation model consisting of Vehicle-to-Vehicle(V2V) links is constructed.Each V2V link is regarded as an agent to model this problem as a Markov decision process.Then the SAC reinforcement learning algorithm is used to design a neural network.The agents are trained by maximum entropy and cumulative reward, so the V2V links can optimize the allocation of spectrum resources through rounds of learning.Simulation results show that compared with spectrum resource allocation scheme based on Deep Q-Network(DQN) and Deep Deterministic Policy Gradient(DDPG), the proposed scheme can more efficiently implement spectrum sharing between V2V links, and improves the channel transmission rate and the success rate of payload delivery.
With operating systems being at the core of computer systems, decades of research and engineering efforts have been put into the development of OSes. To keep pace with the speed of modern hardware and application evolvement, we argue that a different approach should be taken in future OS development. Instead of relying solely on human wisdom, we should also leverage AI and machine learning techniques to automatically "learn" how to build and tune an OS. This paper explores the opportunities and challenges of the "learned" OS approach and makes recommendation for future researchers and practitioners on building such an OS.
This book is a technical, historical, and conceptual investigation into the three main methodological approaches to the computational sciences: mathematical, engineering, and experimental. Part I explores the background behind the formal understanding of computing, originating at the end of the nineteenth century, and it invesitagtes the formal origins and conceptual development of the notions of computation, algorithm, and program.Part II overviews the construction of physical devices to performautomated tasks and it considers associated technical and conceptual issues. It starts with the design and construction of the first generation of computingmachines, explores their evolution and progress in engineering (for both hardware and software), and investigates their theoretical and conceptual problems. Part III analyses the methods and principles of experimental sciences founded on computationalmethods. It studies the use ofmachines to performscientific tasks,with particular reference to computer models and simulations. Each part aims at defining a notion of computational validity according to the corresponding methodological approach.
Eco-efficiency plays a crucial role in evaluating the green development of economics. Using the super slack-based measure approach, which considers carbon emissions and three kinds of industrial waste as undesirable outputs, this study examines the eco-efficiency of 48 cities in the Bohai Rim from year 2005 to 2015. Eco-efficiency exhibites a pattern of polarized disparity, with observable increase regional difference. Nonlinear time-varying factor and dynamic panel models are used to analyze the evolution and convergence of eco-efficiency. Analysis of club convergence revealed that the Bohai Rim area is divided into five groups by values and yearly changes of eco-efficiency. The analysis revealed that two groups show strong eco-efficiency and obvious growth paths, while the eco-efficiencies for the other three groups are weak, with stagnant growth paths. The results confirm the existence of relative ß convergence through ordinary least squares and generalized methods of moments, and suggest that economic growth, foreign direct investment and fiscal decentralization have positive influences on eco-efficiency, while the development of secondary industry has negative effects on eco-efficiency.
Chemical engineering, Computer engineering. Computer hardware