Protein structure prediction is important for understanding their function and behavior. This review study presents a comprehensive review of the computational models used in predicting protein structure. It covers the progression from established protein modeling to state-of-the-art artificial intelligence (AI) frameworks. The paper will start with a brief introduction to protein structures, protein modeling, and AI. The section on established protein modeling will discuss homology modeling, ab initio modeling, and threading. The next section is deep learning-based models. It introduces some state-of-the-art AI models, such as AlphaFold (AlphaFold, AlphaFold2, AlphaFold3), RoseTTAFold, ProteinBERT, etc. This section also discusses how AI techniques have been integrated into established frameworks like Swiss-Model, Rosetta, and I-TASSER. The model performance is compared using the rankings of CASP14 (Critical Assessment of Structure Prediction) and CASP15. CASP16 is ongoing, and its results are not included in this review. Continuous Automated Model EvaluatiOn (CAMEO) complements the biennial CASP experiment. Template modeling score (TM-score), global distance test total score (GDT_TS), and Local Distance Difference Test (lDDT) score are discussed too. This paper then acknowledges the ongoing difficulties in predicting protein structure and emphasizes the necessity of additional searches like dynamic protein behavior, conformational changes, and protein–protein interactions. In the application section, this paper introduces some applications in various fields like drug design, industry, education, and novel protein development. In summary, this paper provides a comprehensive overview of the latest advancements in established protein modeling and deep learning-based models for protein structure predictions. It emphasizes the significant advancements achieved by AI and identifies potential areas for further investigation.

The success of ChatGPT is reshaping the landscape of the entire IT industry. The large language model (LLM) powering ChatGPT is experiencing rapid development, marked by enhanced features, improved accuracy, and reduced latency. Due to the execution overhead of LLMs, prevailing commercial LLM products typically manage user queries on remote servers. However, the escalating volume of user queries and the growing complexity of LLMs have led to servers becoming bottlenecks, compromising the quality of service (QoS). To address this challenge, a potential solution is to shift LLM inference services to edge devices, a strategy currently being explored by industry leaders such as Apple, Google, Qualcomm, Samsung, and others. Beyond alleviating the computational strain on servers and enhancing system scalability, deploying LLMs at the edge offers additional advantages. These include real-time responses even in the absence of network connectivity and improved privacy protection for customized or personal LLMs. This article delves into the challenges and potential bottlenecks currently hindering the effective deployment of LLMs on edge devices. Through deploying the LLaMa-2 7B model with INT4 quantization on diverse edge devices and systematically analyzing experimental results, we identify insufficient memory and/or computing resources on traditional edge devices as the primary obstacles. Based on our observation and empirical analysis, we further provide insights and design guidance for the next generation of edge devices and systems from both hardware and software directions.

Due to high leakage current, Dennard scaling has reached its limit on conventional semiconductor technology. Energy reduction at the transistor level by simply lowering supply voltage has proven to be infeasible for these devices. Some recently proposed millivolt switch techniques aim to mitigate these issues. However, Vdd reduction is constrained by high intermittent error probabilities in millivolt switches. Energy-efficient microarchitectures that are computationally error-tolerant are therefore urgently needed. This paper systematically leverages the properties of Redundant Residue Number Systems(RRNS) by varying the number of non-redundant (n) and redundant (r) residues. First, we propose a scalable RRNS microarchitecture that simultaneously supports both, error-correction, as well as checkpointing&restart capabilities. Second, we design novel RRNS-based adaptive checkpointing&restart mechanisms that automatically guarantee reliability while minimizing the energy-delay product(EDP). To the best of our knowledge, these are the first set of checkpointing mechanisms targeting the RRNS infrastructure. Moreover, these mechanisms optimize the usage efficiency of memory capacity. Third, we systematically explore the RRNS design space to find the best (n, r) configuration point. For similar reliability when compared to a conventional binary core, the proposed RRNS scalable microarchitecture reduces EDP by 53% on average for memory-intensive workloads and by 67% on average for non-memory-intensive workloads.

In recent years, integrating distributed energy resources has emerged as a pervasive trend in competitive energy markets. The idea of virtual power plants (VPPs) has gained traction among researchers and startups, offering a solution to address diverse social, economic, and environmental requirements. A VPP comprises interconnected distributed energy resources collaborating to optimize operations and participate in energy markets. However, existing VPPs confront numerous challenges, including the unpredictability of renewable energy sources, the intricacies and fluctuations of energy markets, and concerns related to insecure communication and data transmission. This article comprehensively reviews the concept, historical development, evolution, and components of VPPs. It delves into the various issues and challenges encountered by current VPPs. Furthermore, the article explores the potential of artificial intelligence (AI) in mitigating these challenges, investigating how AI can enhance the performance, efficiency, and sustainability of future smart VPPs.

Residue Number Systems (RNS) demonstrate the fascinating potential to serve integer addition/ multiplication-intensive applications. The complexity of Artificial Intelligence (AI) models has grown enormously in recent years. From a computer system’s perspective, ensuring the training of these large-scale AI models within an adequate time and energy consumption has become a big concern. Matrix multiplication is a dominant subroutine in many prevailing AI models, with an addition/multiplication-intensive attribute. However, the data type of matrix multiplication within machine learning training typically requires real numbers, which indicates that RNS benefits for integer applications cannot be directly gained by AI training. The state-of-the-art RNS real-number encodings, including floating-point and fixed-point, have defects and can be further enhanced. To transform default RNS benefits to the efficiency of large-scale AI training, we propose a low-cost and high-accuracy RNS fixed-point representation: Single RNS Logical Partition (S-RNS-Logic-P) representation with Scaling-down Postprocessing Multiplication (SD-Post-Mul) . Moreover, we extend the implementation details of the other two RNS fixed-point methods: Double RNS Concatenation and S-RNS-Logic-P representation with Scaling-down Preprocessing Multiplication . We also design the architectures of these three fixed-point multipliers. In empirical experiments, our S-RNS-Logic-P representation with SD-Post-Mul method achieves less latency and energy overhead while maintaining good accuracy. Furthermore, this method can easily extend to the Redundant Residue Number System to raise the efficiency of error-tolerant domains, such as improving the error correction efficiency of quantum computing.

Federated Learning is a novel paradigm allowing training of a global machine-learning model on distributed devices. It shares model parameters instead of the private raw data during the entire model training process. While Federated Learning enables machine learning processes to take place collaboratively on the Internet of Things (IoT) devices, compared to data centers, IoT devices with limited resource budgets typically have less security protection and are more vulnerable to potential cyber-attacks. Current research on the evaluation of Federated Learning is mainly based on simulation of multi-clients/processes on a single machine/device. However, there is a gap in understanding the performance of Federated Learning under cyber-attacks in real-world distributed low-power IoT devices. In this paper, we are among the first to evaluate the performance of Federated Learning under thermal stress on real-world IoT-based distributed systems. We conducted comprehensive experiments using the CIFAR-10 dataset and various performance metrics including training time, CPU and GPU utilization rate, temperature, and power consumption. The experimental results demonstrate that thermal stress is effective on IoT-based Federated Learning systems as the entire global model and device performance degrade when even a small ratio of IoT devices are being impacted.