Automatic Tool-Flow for Mapping Applications to an Application-Specific CGRA Architecture

Fricke, Florian; Werner, André; Shahin, Keyvan; Werner, Felix; Hübner, Michael

doi:10.1109/ipdpsw.2019.00033

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…The VCGRA architecture, for example, enables the creation of parameterizable CGRA-Architectures, that can be suited to a specific application by configuring the connections inside the CGRA and the capabilities of the processing elements [17]. It has been designed to be used on top of commercial FPGA and comes with a complete toolflow for designing the FPGA configuration and the interfaces in hardand software.…”

Section: Discussion: Embedded Process Controlmentioning

confidence: 99%

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Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy Using a Novel Data Augmentation Method

Fricke

Brandalero

Liehr

et al. 2022

IEEE Trans. Emerg. Topics Comput.

Self Cite

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Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are valuable analytical and quality control methods for most industrial chemical processes as they provide information on the concentrations of individual compounds and by-products. These processes are traditionally carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been realized to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra, to train an ANN with better prediction performance and speed than state-of-the-art analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.

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Section: Discussion: Embedded Process Controlmentioning

confidence: 99%

“…The most important NMR-active nuclei are the isotopes 1 H and 13 C, as they are found in most molecules of interest. Other important nuclei are 19 F, 31 P, 17 O, 15 N and some metals.…”

Section: Nmr Spectroscopymentioning

confidence: 99%

Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy Using a Novel Data Augmentation Method

Fricke

Brandalero

Liehr

et al. 2022

IEEE Trans. Emerg. Topics Comput.

Self Cite

View full text Add to dashboard Cite

show abstract

“…It consists of reconfigurable PEs which are connected to each other using reconfigurable Virtual Switch Blocks (VSBs). In the CGRA tool flow (right-hand side of Figure 2), the user will determine the CGRA settings that will configure the required configurable components of the CGRA to realize the desired application [22,23]. In CGRA, PE is the main unit that executes the application and can be configured by IP cores, Functional Units (FU) or Arithmetic Units (AU) with different granularities.…”

Section: Coarse-grained Reconfigurable Arraymentioning

confidence: 99%

Towards Trust Hardware Deployment of Edge Computing: Mitigation of Hardware Trojans Based on Evolvable Hardware

Wang

Huang

et al. 2022

Applied Sciences

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Hardware Trojans (HTs) are malicious hardware components designed to leak confidential information or cause the chip/circuit on which they are integrated to malfunction during operation. When we deploy such hardware platforms for edge computing, FPGA-based implementations of Coarse-Grained Reconfigurable Array (CGRA) are also currently falling victim to HT insertion. However, for CGRA, an evolvable hardware (EHW) platform, which has the ability to dynamically change its configuration and behavioral characteristics based on inputs from the environment, provides us with a new way to mitigate HT attacks. In this regard, we investigate the feasibility of using EHW to mitigate HTs that disrupt normal functionality in CGRA in this paper. When it is determined that HT is inserted into certain processing elements (PEs), the array autonomously reconfigures the circuit structure based on an evolutionary algorithm (EA) to avoid the use of HT-infected (HT-I) PEs. We show that the proposed approach is applicable to: (1) hardware platforms that support coarse-grained reconfiguration; and (2) pure combinatorial circuits. In a simulation environment built in Python, this paper reports experimental results for two target evolutionary circuits and outlines the effectiveness of the proposed method.

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“…Fricke et al present an automated toolflow that utilizes a CGRA as an overlay architecture on FPGA [5]. The authors also investigate the toolflow usability in terms of tool runtime [6]. However, it appears that the architecture does not support control flow and is lacking a verified realization on a chip.…”

Section: Related Workmentioning

confidence: 99%

UltraSynth: Insights of a CGRA Integration into a Control Engineering Environment

Wolf

Engel

Ruschke

et al. 2021

J Sign Process Syst

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Coarse Grained Reconfigurable Arrays (CGRAs) or Architectures are a concept for hardware accelerators based on the idea of distributing workload over Processing Elements. These processors exploit instruction level parallelism, while being energy efficient due to their simplistic internal structure. However, the incorporation into a complete computing system raises severe challenges at the hardware and software level. This article evaluates a CGRA integrated into a control engineering environment targeting a Xilinx Zynq System on Chip (SoC) in detail. Besides the actual application execution performance, the practicability of the configuration toolchain is validated. Challenges of the real-world integration are discussed and practical insights are highlighted.

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Automatic Tool-Flow for Mapping Applications to an Application-Specific CGRA Architecture

Cited by 4 publications

References 9 publications

Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy Using a Novel Data Augmentation Method

Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy Using a Novel Data Augmentation Method

Towards Trust Hardware Deployment of Edge Computing: Mitigation of Hardware Trojans Based on Evolvable Hardware

UltraSynth: Insights of a CGRA Integration into a Control Engineering Environment

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