Trusted IP solution in multi-tenant cloud FPGA platform

Because FPGAs outperform traditional processing cores like CPUs and GPUs in terms of performance per watt and flexibility, they are being used more and more in cloud and data center applications. There are growing worries about the security risks posed by multi-tenant sharing as the demand for hardware acceleration increases and gradually gives way to FPGA multi-tenancy in the cloud. The confidentiality, integrity, and availability of FPGA-accelerated applications may be compromised if space-shared FPGAs are made available to many cloud tenants. We propose a root of trust-based trusted execution mechanism called \textbf{TrustToken} to prevent harmful software-level attackers from getting unauthorized access and jeopardizing security. With safe key creation and truly random sources, \textbf{TrustToken} creates a security block that serves as the foundation of trust-based IP security. By offering crucial security characteristics, such as secure, isolated execution and trusted user interaction, \textbf{TrustToken} only permits trustworthy connection between the non-trusted third-party IP and the rest of the SoC environment. The suggested approach does this by connecting the third-party IP interface to the \textbf{TrustToken} Controller and running run-time checks on the correctness of the IP authorization(Token) signals. With an emphasis on software-based assaults targeting unauthorized access and information leakage, we offer a noble hardware/software architecture for trusted execution in FPGA-accelerated clouds and data centers

A Kernel-Based Approach for Biomedical Named Entity Recognition

Support vector machine (SVM) is one of the popular machine learning techniques used in various text processing tasks including named entity recognition (NER). The performance of the SVM classifier largely depends on the appropriateness of the kernel function. In the last few years a number of task-specific kernel functions have been proposed and used in various text processing tasks, for example, string kernel, graph kernel, tree kernel and so on. So far very few efforts have been devoted to the development of NER task specific kernel. In the literature we found that the tree kernel has been used in NER task only for entity boundary detection or reannotation. The conventional tree kernel is unable to execute the complete NER task on its own. In this paper we have proposed a kernel function, motivated by the tree kernel, which is able to perform the complete NER task. To examine the effectiveness of the proposed kernel, we have applied the kernel function on the openly available JNLPBA 2004 data. Our kernel executes the complete NER task and achieves reasonable accuracy

Multi-Tenant Cloud FPGA: A Survey on Security

With the exponentially increasing demand for performance and scalability in cloud applications and systems, data center architectures evolved to integrate heterogeneous computing fabrics that leverage CPUs, GPUs, and FPGAs. FPGAs differ from traditional processing platforms such as CPUs and GPUs in that they are reconfigurable at run-time, providing increased and customized performance, flexibility, and acceleration. FPGAs can perform large-scale search optimization, acceleration, and signal processing tasks compared with power, latency, and processing speed. Many public cloud provider giants, including Amazon, Huawei, Microsoft, Alibaba, etc., have already started integrating FPGA-based cloud acceleration services. While FPGAs in cloud applications enable customized acceleration with low power consumption, it also incurs new security challenges that still need to be reviewed. Allowing cloud users to reconfigure the hardware design after deployment could open the backdoors for malicious attackers, potentially putting the cloud platform at risk. Considering security risks, public cloud providers still don't offer multi-tenant FPGA services. This paper analyzes the security concerns of multi-tenant cloud FPGAs, gives a thorough description of the security problems associated with them, and discusses upcoming future challenges in this field of study

LLM for SoC Security: A Paradigm Shift

As the ubiquity and complexity of system-on-chip (SoC) designs increase across electronic devices, the task of incorporating security into an SoC design flow poses significant challenges. Existing security solutions are inadequate to provide effective verification of modern SoC designs due to their limitations in scalability, comprehensiveness, and adaptability. On the other hand, Large Language Models (LLMs) are celebrated for their remarkable success in natural language understanding, advanced reasoning, and program synthesis tasks. Recognizing an opportunity, our research delves into leveraging the emergent capabilities of Generative Pre-trained Transformers (GPTs) to address the existing gaps in SoC security, aiming for a more efficient, scalable, and adaptable methodology. By integrating LLMs into the SoC security verification paradigm, we open a new frontier of possibilities and challenges to ensure the security of increasingly complex SoCs. This paper offers an in-depth analysis of existing works, showcases practical case studies, demonstrates comprehensive experiments, and provides useful promoting guidelines. We also present the achievements, prospects, and challenges of employing LLM in different SoC security verification tasks.Comment: 42 page

LLM for SoC Security: A Paradigm Shift

As the ubiquity and complexity of system-on-chip (SoC) designs increase across electronic devices, the task of incorporating security into an SoC design flow poses significant challenges. Existing security solutions are inadequate to provide effective verification of modern SoC designs due to their limitations in scalability, comprehensiveness, and adaptability. On the other hand, Large Language Models (LLMs) are celebrated for their remarkable success in natural language understanding, advanced reasoning, and program synthesis tasks. Recognizing an opportunity, our research delves into leveraging the emergent capabilities of Generative Pre-trained Transformers (GPTs) to address the existing gaps in SoC security, aiming for a more efficient, scalable, and adaptable methodology. By integrating LLMs into the SoC security verification paradigm, we open a new frontier of possibilities and challenges to ensure the security of increasingly complex SoCs. This paper offers an in-depth analysis of existing works, showcases practical case studies, demonstrates comprehensive experiments, and provides useful promoting guidelines. We also present the achievements, prospects, and challenges of employing LLM in different SoC security verification tasks

Depression among the Non-Native International Undergraduate Students Studying Dentistry in Bangladesh.

BACKGROUND: Bangladesh has been attracting international students with interests in various subjects recently. Every year students from different parts of the world come to study undergraduate and postgraduate courses, mostly at private universities in Bangladesh. This study evaluates the depression status among international students who are studying dentistry in Bangladesh. METHODS: This cross-sectional survey was conducted among International undergraduate dental students who enrolled in the Bachelor of Dental Surgery program in nine public and private dental colleges in Bangladesh. Participants were selected using a convenience sampling method. A total of 206 students completed the survey where 78.5% of them were female students and 21.5% students were male, and a CES-D 10-item Likert scale questionnaire was used for data collection. The Cronbach alpha for the 10-item CES-D scale for this population is 0.812. RESULTS: The majority of the students (79.5%) are below 24 years of age with a mean age of 23.22 years and standard deviation of 2.3, and are students who cannot communicate well in Bengali (Bangla), about 60% of them have experienced depression. About 77.3% (p < 0.00) of the international students having financial difficulties exhibited depression. The international students who went through financial problems were two times more likely to suffer from depression (OR = 2.38; p-value < 0.01). CONCLUSION: This study tried to highlight the struggles faced by international students in Bangladesh studying dentistry. It is evident from the findings that several factors influence students' mental well-being during demanding dental education years

Spatio-Temporal GPU Management for Real-Time Cyber-Physical Systems

General-purpose Graphics Processing Units (GPUs) have been considered as a promising technology to address the high computational demands of real-time data-intensive applications. Many of today's embedded processors already provide on-chip GPUs, the use of which can greatly help satisfy the timing challenges of data-intensive tasks by accelerating their executions. However, the current state-of-the-art GPU management in real-time systems still lacks properties required for efficient and certifiable real-time GPU computing. For example, existing real-time systems sequentially execute GPU workloads to guarantee predictable GPU access time, which significantly underutilizes the GPU and exacerbates temporal dependency among the workloads.In this research, we propose a spatial-temporal GPU management framework for real-time cyber-physical systems. Our proposed framework explicitly manages the allocation of GPU's internal execution engines. This approach allows multiple GPU-using tasks to simultaneously execute on the GPU, thereby improving GPU utilization and reducing response time. Also, it can improve temporal isolation by allocating a portion of the GPU execution engines to tasks for their exclusive use. We have implemented a prototype of the proposed framework for a CUDA environment. The case study using this implementation on two NVIDIA GPUs, GeForce 970 and Jetson TX2, shows that our framework reduces the response time of GPU execution segments in a predictable manner, by executing them in parallel. Experimental results with randomly-generated tasksets indicate that our framework yields a significant benefit in schedulability compared to the existing approach

Reverse microemulsion mediated sol-gel synthesis of lithium silicate nanoparticles under ambient conditions: scope for CO2 sequestration

We report on the synthesis of nanocrystalline lithium silicate by coupling of sol–gel method in reverse microemulsion. The sample calcined at 800 ºC gives pure phase lithium metasilicate nanocrystallites. X-ray diffraction and transmission electron microscopy confirmed the formation of nanocrystalline lithium silicate particles with a narrow size distribution. The nanoparticle prepared in the microemulsion shows enhanced CO2 sorption capacity and shorter retention times at higher temperature (~ 131 ml/g at STP at 610 ºC) which are better than the best known results

Automatic selection of informative sentences: The sentences that can generate multiple choice questions

Traditional education cannot meet the expectation and requirement of a Smart City; it require more advance forms like active learning, ICT education etc. Multiple choice questions (MCQs) play an important role in educational assessment and active learning which has a key role in Smart City education. MCQs are effective to assess the understanding of well-defined concepts. A fraction of all the sentences of a text contain well-defined concepts or information that can be asked as a MCQ. These informative sentences are required to be identified first for preparing multiple choice questions manually or automatically. In this paper we propose a technique for automatic identification of such informative sentences that can act as the basis of MCQ. The technique is based on parse structure similarity. A reference set of parse structures is compiled with the help of existing MCQs. The parse structure of a new sentence is compared with the reference structures and if similarity is found then the sentence is considered as a potential candidate. Next a rule-based post-processing module works on these potential candidates to select the final set of informative sentences. The proposed approach is tested in sports domain, where many MCQs are easily available for preparing the reference set of structures. The quality of the system selected sentences is evaluated manually. The experimental result shows that the proposed technique is quite promising

Use of global context for handling noisy names in discussion texts of a homeopathy discussion forum

The task of identifying named entities from the discussion texts in Web forums faces the challenge of noisy names. As the names are often misspelled or abbreviated, the conventional techniques have failed to detect the noisy names properly. In this paper we propose a global context based framework for handling the noisy names. The framework is tested on a named entity recognition system designed to identify the names from the discussion texts in a homeopathy diagnosis discussion forum. The proposed global context-based framework is found to be effective in improving the accuracy of the named entity recognition system