scholarly journals A Data-Flow Soft-Core Processor for Accelerating Scientific Calculation on FPGAs

2016 ◽  
Vol 2016 ◽  
pp. 1-21 ◽  
Author(s):  
Lorenzo Verdoscia ◽  
Roberto Giorgi

We present a new type of soft-core processor called the “Data-Flow Soft-Core” that can be implemented through FPGA technology with adequate interconnect resources. This processor provides data processing based on data-flow instructions rather than control flow instructions. As a result, during an execution on the accelerator of the Data-Flow Soft-Core, both partial data and instructions are eliminated as traffic for load and store activities. Data-flow instructions serve to describe a program and to dynamically change the context of a data-flow program graph inside the accelerator, on-the-fly. Our proposed design aims at combining the performance of a fine-grained data-flow architecture with the flexibility of reconfiguration, without requiring a partial reconfiguration or new bit-stream for reprogramming it. The potential of the data-flow implementation of a function or functional program can be exploited simply by relying on its description through the data-flow instructions that reprogram the Data-Flow Soft-Core. Moreover, the data streaming process will mirror those present in other FPGA applications. Finally, we show the advantages of this approach by presenting two test cases and providing the quantitative and numerical results of our evaluations.

2012 ◽  
Vol 490-495 ◽  
pp. 594-597
Author(s):  
Cheng Qun Li ◽  
Liang Gao

This paper introduces a new type of automatic steel bundling machine for bundling process, which includes a pneumatic action process, mainly do some researches on the pneumatic control system. The system chooses PLC as the core control component, puts forward the hardware of control system and control flow. Eventually we have been designed the control program.


2020 ◽  
Vol 14 (3) ◽  
pp. 391-403
Author(s):  
Dimitris Palyvos-Giannas ◽  
Bastian Havers ◽  
Marina Papatriantafilou ◽  
Vincenzo Gulisano

Data streaming enables online monitoring of large and continuous event streams in Cyber-Physical Systems (CPSs). In such scenarios, fine-grained backward provenance tools can connect streaming query results to the source data producing them, allowing analysts to study the dependency/causality of CPS events. While CPS monitoring commonly produces many events, backward provenance does not help prioritize event inspection since it does not specify if an event's provenance could still contribute to future results. To cover this gap, we introduce Ananke , a framework to extend any fine-grained backward provenance tool and deliver a live bipartite graph of fine-grained forward provenance. With Ananke , analysts can prioritize the analysis of provenance data based on whether such data is still potentially being processed by the monitoring queries. We prove our solution is correct, discuss multiple implementations, including one leveraging streaming APIs for parallel analysis, and show Ananke results in small overheads, close to those of existing tools for fine-grained backward provenance.


2021 ◽  
Vol 5 (OOPSLA) ◽  
pp. 1-30
Author(s):  
Son Tuan Vu ◽  
Albert Cohen ◽  
Arnaud De Grandmaison ◽  
Christophe Guillon ◽  
Karine Heydemann

Software protections against side-channel and physical attacks are essential to the development of secure applications. Such protections are meaningful at machine code or micro-architectural level, but they typically do not carry observable semantics at source level. This renders them susceptible to miscompilation, and security engineers embed input/output side-effects to prevent optimizing compilers from altering them. Yet these side-effects are error-prone and compiler-dependent. The current practice involves analyzing the generated machine code to make sure security or privacy properties are still enforced. These side-effects may also be too expensive in fine-grained protections such as control-flow integrity. We introduce observations of the program state that are intrinsic to the correct execution of security protections, along with means to specify and preserve observations across the compilation flow. Such observations complement the input/output semantics-preservation contract of compilers. We introduce an opacification mechanism to preserve and enforce a partial ordering of observations. This approach is compatible with a production compiler and does not incur any modification to its optimization passes. We validate the effectiveness and performance of our approach on a range of benchmarks, expressing the secure compilation of these applications in terms of observations to be made at specific program points.


2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Yadi Wang ◽  
Wangyang Yu ◽  
Peng Teng ◽  
Guanjun Liu ◽  
Dongming Xiang

With the development of smart devices and mobile communication technologies, e-commerce has spread over all aspects of life. Abnormal transaction detection is important in e-commerce since abnormal transactions can result in large losses. Additionally, integrating data flow and control flow is important in the research of process modeling and data analysis since it plays an important role in the correctness and security of business processes. This paper proposes a novel method of detecting abnormal transactions via an integration model of data and control flows. Our model, called Extended Data Petri net (DPNE), integrates the data interaction and behavior of the whole process from the user logging into the e-commerce platform to the end of the payment, which also covers the mobile transaction process. We analyse the structure of the model, design the anomaly detection algorithm of relevant data, and illustrate the rationality and effectiveness of the whole system model. Through a case study, it is proved that each part of the system can respond well, and the system can judge each activity of every mobile transaction. Finally, the anomaly detection results are obtained by some comprehensive analysis.


2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Weizhong Qiang ◽  
Shizhen Wang ◽  
Hai Jin ◽  
Jiangying Zhong

A cyber-physical system (CPS) is known as a mix system composed of computational and physical capabilities. The fast development of CPS brings new security and privacy requirements. Code reuse attacks that affect the correct behavior of software by exploiting memory corruption vulnerabilities and reusing existing code may also be threats to CPS. Various defense techniques are proposed in recent years as countermeasures to emerging code reuse attacks. However, they may fail to fulfill the security requirement well because they cannot protect the indirect function calls properly when it comes to dynamic code reuse attacks aiming at forward edges of control-flow graph (CFG). In this paper, we propose P-CFI, a fine-grained control-flow integrity (CFI) method, to protect CPS against memory-related attacks. We use points-to analysis to construct the legitimate target set for every indirect call cite and check whether the target of the indirect call cite is in the legitimate target set at runtime. We implement a prototype of P-CFI on LLVM and evaluate both its functionality and performance. Security analysis proves that P-CFI can mitigate the dynamic code reuse attack based on forward edges of CFG. Performance evaluation shows that P-CFI can protect CPS from dynamic code reuse attacks with trivial time overhead between 0.1% and 3.5% (Copyright © 2018 John Wiley & Sons, Ltd.).


2018 ◽  
Vol 2018 ◽  
pp. 1-1
Author(s):  
Weizhong Qiang ◽  
Shizhen Wang ◽  
Hai Jin ◽  
Jiangying Zhong

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