As NAND flash memory has become a major choice of storage media in diversified computing environments, the performance issue of flash memory has been extensively addressed in many excellent designs. Among them, an eff...
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ISBN:
(纸本)9781479939541
As NAND flash memory has become a major choice of storage media in diversified computing environments, the performance issue of flash memory has been extensively addressed in many excellent designs. Among them, an effective strategy is to adopt multiple channels and flash-memory chips to improve the performance on data accesses. However, the degree of data access parallelism cannot be increased by simply increasing the number of channels and chips in the storage device, because it is seriously limited by the maximum current constraint of the bus interface and affected by the access patterns of user data. As a consequence, to maximize the degree of access parallelism, it is of paramount significance to have a proper scheduling strategy to determine the order that read/write requests are served. In this paper, a current-aware scheduling strategy for read/write requests is proposed to maximize the read performance without violating the bus current constraint and without missing (the deadline of) written data. The proposed strategy is then evaluated through a series of experiments, in which the results are quite encouraging.
Recently, storage systems have observed a great leap in performance, reliability, endurance, and cost, due to the advance in non-volatile memory technologies, such as NAND flash memory. However, although delivering be...
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ISBN:
(纸本)9781479957125
Recently, storage systems have observed a great leap in performance, reliability, endurance, and cost, due to the advance in non-volatile memory technologies, such as NAND flash memory. However, although delivering better performance, shock resistance, and energy efficiency than mechanical hard disks, NAND flash memory comes with unique characteristics and operational constraints, and cannot be directly used as an ideal block device. In particular, to address the notorious write-once property, garbage collection is necessary to clean the outdated data on flash memory. However, garbage collection is very time-consuming and often becomes the performance bottleneck of flash memory. Moreover, because flash memory cells endure very limited writes (as compared to mechanical hard disks) before they are worn out, the wear-leveling design is also indispensable to equalize the use of flash memory space and to prolong the flash memory lifetime. In response, this paper surveys state-of-the-art garbage collection and wear-leveling designs, so as to assist the design of flash memory management in various application scenarios. The future development trends of flash memory, such as the widespread adoption of higher-level flash memory and the emerging of three-dimensional (3D) flash memory architectures, are also discussed.
Recommendation systems are a popular marketing strategy for online service providers. These systems predict a customer's future preferences from the past behaviors of that customer and the other customers. Most of...
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Recommendation systems are a popular marketing strategy for online service providers. These systems predict a customer's future preferences from the past behaviors of that customer and the other customers. Most of the popular online stores process millions of transactions per day; therefore, providing quick and quality recommendations using the large amount of data collected from past transactions can be challenging. Parallel processing power of GPUs can be used to accelerate the recommendation process. However, the amount of memory available on a GPU card is limited; thus, a number of passes may be required to completely process a large-scale dataset. This paper proposes two parallel, item-based recommendation algorithms implemented using the CUDA platform. Considering the high sparsity of the user-item data, we utilize two compression techniques to reduce the required number of passes and increase the speedup. The experimental results on synthetic and real-world datasets show that our algorithms outperform the respective CPU implementations and also the naïve GPU implementation which does not use compression.
In this paper, we introduce a reference model that has compatibility with olfactory information presentation and define metadata for olfactory interaction by analyzing various elements needed in olfactory display tech...
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ISBN:
(纸本)9781457717666
In this paper, we introduce a reference model that has compatibility with olfactory information presentation and define metadata for olfactory interaction by analyzing various elements needed in olfactory display technology and propose an olfactory interaction model and define metadata for digital information of scent generator and how it can be used. As an application, we designed an interactive virtual space for the multi-purpose hall in Song-Do Future City located in Incheon, Korea.
Recently, large language models (LLMs) have demonstrated promising applications in the autonomous driving (AD) domain, including language-based interactions and decision-making. Ensuring they safely handle harmful inp...
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Recently, large language models (LLMs) have demonstrated promising applications in the autonomous driving (AD) domain, including language-based interactions and decision-making. Ensuring they safely handle harmful inputs is crucial before formal deployment. However, research reveals emerging hand-crafted jailbreak attacks, which pack harmful prompts into harmless instructions, can bypass LLMs’ security mechanisms and elicit harmful responses. To deeply understand such jailbreaks, this paper introduces a Compositional Instruction Attack (CIA) framework to generalize them, and develop two CIA jailbreaking methods to automatically generate tailored jailbreak prompts for each harmful prompt. Then, this paper builds the first CIA question-answering (CIAQA) dataset with 2.7K multiple-choice questions of 900 successful jailbreaks, for assessing LLMs’ ability to identify underlying harmful intents, harmfulness, and task priority in CIA jailbreaks. Combined with experimental analysis on CIAQA and other datasets, this paper concludes three possible reasons for the failure of LLM defenses against CIAs. Finally, we propose an intent-based defense paradigm (IBD), enabling LLMs to defend against CIA by leveraging its capability to identify intents. Experimental results show CIA can achieve attack success rates (ASR) of 95%+ and 85%+ in AD and common harmful scenarios for three well-known LLMs (GPT-4, GPT-3.5, and Llama2-70b-chat), and IBD reduces ASR by 74%+.
Distributed Collaborative Machine Learning (DCML) has emerged in artificial intelligence-empowered edge computing environments, such as the Industrial Internet of Things (IIoT), to process tremendous data generated by...
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Distributed Collaborative Machine Learning (DCML) has emerged in artificial intelligence-empowered edge computing environments, such as the Industrial Internet of Things (IIoT), to process tremendous data generated by smart devices. However, parallel DCML frameworks require resource-constrained devices to update the entire Deep Neural Network (DNN) models and are vulnerable to reconstruction attacks. Concurrently, the serial DCML frameworks suffer from training efficiency problems due to their serial training nature. In this paper, we propose a Model Pruning-enabled Federated Split Learning framework (MP-FSL) to reduce resource consumption with a secure and efficient training scheme. Specifically, MP-FSL compresses DNN models by adaptive channel pruning and splits each compressed model into two parts that are assigned to the client and the server. Meanwhile, MP-FSL adopts a novel aggregation algorithm to aggregate the pruned heterogeneous models. We implement MP-FSL with a real FL platform to evaluate its performance. The experimental results show that MP-FSL outperforms the state-of-the-art frameworks in model accuracy by up to 1.35%, while concurrently reducing storage and computational resource consumption by up to 32.2% and 26.73%, respectively. These results demonstrate that MP-FSL is a comprehensive solution to the challenges faced by DCML, with superior performance in both reduced resource consumption and enhanced model performance.
Graph pattern mining is essential for deciphering complex networks. In the real world, graphs are dynamic and evolve over time, necessitating updates in mining patterns to reflect these changes. Traditional methods us...
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Graph pattern mining is essential for deciphering complex networks. In the real world, graphs are dynamic and evolve over time, necessitating updates in mining patterns to reflect these changes. Traditional methods use fine-grained incremental computation to avoid full re-mining after each update, which improves speed but often overlooks potential gains from examining inter-update interactions holistically, thus missing out on overall efficiency *** this paper, we introduce Cheetah, a dynamic graph mining system that processes updates in a coarse-grained manner by leveraging exploration domains. These domains exploit the community structure of real-world graphs to uncover data reuse opportunities typically missed by existing approaches. Exploration domains, which encapsulate extensive portions of the graph relevant to updates, allow multiple updates to explore the same regions efficiently. Cheetah dynamically constructs these domains using a management module that identifies and maintains areas of redundancy as the graph changes. By grouping updates within these domains and employing a neighbor-centric expansion strategy, Cheetah minimizes redundant data accesses. Our evaluation of Cheetah across five real-world datasets shows it outperforms current leading systems by an average factor of 2.63 ×.
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