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2022 ◽  
Vol 9 (1) ◽  
pp. 011403
Author(s):  
Suchismita Sarker ◽  
Robert Tang-Kong ◽  
Rachel Schoeppner ◽  
Logan Ward ◽  
Naila Al Hasan ◽  
...  

2022 ◽  
Vol 425 ◽  
pp. 127991
Author(s):  
Dinghui Xiong ◽  
Nuanfei Zhu ◽  
Fang Zhu ◽  
Salome Yakubu ◽  
Jungang Lv ◽  
...  

2022 ◽  
Vol 3 (1) ◽  
pp. 101082
Author(s):  
Daniele Novarina ◽  
Fernando R. Rosas Bringas ◽  
Omar G. Rosas Bringas ◽  
Michael Chang

2024 ◽  
Vol 84 ◽  
Author(s):  
M. F. R. Dias ◽  
F. L. L. Oliveira ◽  
V. S. Pontes ◽  
M. L. Silva

Abstract In recent years, the development of high-throughput technologies for obtaining sequence data leveraged the possibility of analysis of protein data in silico. However, when it comes to viral polyprotein interaction studies, there is a gap in the representation of those proteins, given their size and length. The prepare for studies using state-of-the-art techniques such as Machine Learning, a good representation of such proteins is a must. We present an alternative to this problem, implementing a fragmentation and modeling protocol to prepare those polyproteins in the form of peptide fragments. Such procedure is made by several scripts, implemented together on the workflow we call PolyPRep, a tool written in Python script and available in GitHub. This software is freely available only for noncommercial users.


2022 ◽  
Vol 7 (1) ◽  
pp. 541-543
Author(s):  
Zhenghong Li ◽  
Chengyu Zhang ◽  
Zhengduo Wang ◽  
Chuan Li ◽  
Zhiheng Yang ◽  
...  

2022 ◽  
Vol 27 (2) ◽  
pp. 1-33
Author(s):  
Zahra Ebrahimi ◽  
Dennis Klar ◽  
Mohammad Aasim Ekhtiyar ◽  
Akash Kumar

The rapid evolution of error-resilient programs intertwined with their quest for high throughput has motivated the use of Single Instruction, Multiple Data (SIMD) components in Field-Programmable Gate Arrays (FPGAs). Particularly, to exploit the error-resiliency of such applications, Cross-layer approximation paradigm has recently gained traction, the ultimate goal of which is to efficiently exploit approximation potentials across layers of abstraction. From circuit- to application-level, valuable studies have proposed various approximation techniques, albeit linked to four drawbacks: First, most of approximate multipliers and dividers operate only in SISD mode. Second, imprecise units are often substituted, merely in a single kernel of a multi-kernel application, with an end-to-end analysis in Quality of Results (QoR) and not in the gained performance. Third, state-of-the-art (SoA) strategies neglect the fact that each kernel contributes differently to the end-to-end QoR and performance metrics. Therefore, they lack in adopting a generic methodology for adjusting the approximation knobs to maximize performance gains for a user-defined quality constraint. Finally, multi-level techniques lack in being efficiently supported, from application-, to architecture-, to circuit-level, in a cohesive cross-layer hierarchy. In this article, we propose Plasticine , a cross-layer methodology for multi-kernel applications, which addresses the aforementioned challenges by efficiently utilizing the synergistic effects of a chain of techniques across layers of abstraction. To this end, we propose an application sensitivity analysis and a heuristic that tailor the precision at constituent kernels of the application by finding the most tolerable degree of approximations for each of consecutive kernels, while also satisfying the ultimate user-defined QoR. The chain of approximations is also effectively enabled in a cross-layer hierarchy, from application- to architecture- to circuit-level, through the plasticity of SIMD multiplier-dividers, each supporting dynamic precision variability along with hybrid functionality. The end-to-end evaluations of Plasticine  on three multi-kernel applications employed in bio-signal processing, image processing, and moving object tracking for Unmanned Air Vehicles (UAV) demonstrate 41%–64%, 39%–62%, and 70%–86% improvements in area, latency, and Area-Delay-Product (ADP), respectively, over 32-bit fixed precision, with negligible loss in QoR. To springboard future research in reconfigurable and approximate computing communities, our implementations will be available and open-sourced at https://cfaed.tu-dresden.de/pd-downloads.


2022 ◽  
Vol 3 (1) ◽  
pp. 101075
Author(s):  
Sarah Nahlé ◽  
Laura Quirion ◽  
Jonathan Boulais ◽  
Halil Bagci ◽  
Denis Faubert ◽  
...  

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