Zobrazeno 1 - 10
of 8 608
pro vyhledávání: '"dataflow"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-16 (2024)
Abstract This paper proposes a geometric-based technique for compressing convolutional neural networks to accelerate computations and improve generalization by eliminating non-informative components. The technique utilizes a geometric index called se
Externí odkaz:
https://doaj.org/article/0c04483c62b142d5b1b8fc5194aa6671
Autor:
Dionysios Filippas, Christodoulos Peltekis, Vasileios Titopoulos, Ioannis Kansizoglou, Georgios CH. Sirakoulis, Antonios Gasteratos, Giorgos Dimitrakopoulos
Publikováno v:
IEEE Access, Vol 12, Pp 57194-57208 (2024)
Convolution neural networks (CNNs) are widely applied in many machine learning applications. Hardware acceleration for CNNs is crucial, given their high computational intensity and the demand for enhanced energy efficiency and reduced latency in appl
Externí odkaz:
https://doaj.org/article/33c1766733254d368409fdccc04d5072
Publikováno v:
IEEE Access, Vol 12, Pp 39748-39769 (2024)
This paper presents an approach for reducing the memory requirements of periodically executed dataflow applications, while minimizing the period when deployed on a many-core target. Often, implementations of dataflow applications suffer from data dup
Externí odkaz:
https://doaj.org/article/572379ddeeee4da685128d4115cdbc1c
Publikováno v:
IEEE Access, Vol 12, Pp 10893-10909 (2024)
The presence of sparsity in both input features and weights within convolutional neural networks offers a valuable opportunity to significantly reduce the number of computations required during inference. Moreover, the practice of compressing input d
Externí odkaz:
https://doaj.org/article/fe69c126564b43d980f9fce76ec7a0a6
Publikováno v:
Digital Communications and Networks, Vol 9, Iss 6, Pp 1448-1457 (2023)
Edge computing can alleviate the problem of insufficient computational resources for the user equipment, improve the network processing environment, and promote the user experience. Edge computing is well known as a prospective method for the develop
Externí odkaz:
https://doaj.org/article/67c2cf18fea0453daeb879acfb95bba0
Autor:
Rene Miedema, Christos Strydis
Publikováno v:
Frontiers in Neuroinformatics, Vol 18 (2024)
IntroductionIn-silico simulations are a powerful tool in modern neuroscience for enhancing our understanding of complex brain systems at various physiological levels. To model biologically realistic and detailed systems, an ideal simulation platform
Externí odkaz:
https://doaj.org/article/91cf1a4c41084e51b2cc69acfa6f8751
Autor:
Matthew James Stephenson
Publikováno v:
Software, Vol 2, Iss 3, Pp 427-446 (2023)
The core reasoning task for datalog engines is materialization, the evaluation of a datalog program over a database alongside its physical incorporation into the database itself. The de-facto method of computing is through the recursive application o
Externí odkaz:
https://doaj.org/article/96cf60ca5d80485b8e7509766f65e1ab
Publikováno v:
Journal of Big Data, Vol 10, Iss 1, Pp 1-12 (2023)
Abstract The super power of deep learning in image classification problems have become very popular and applicable in many areas like medical sciences. Some of the medical applications are real-time and may be implemented in embedded devices. In thes
Externí odkaz:
https://doaj.org/article/fe731e3b7bfd4be697f08341af1d0e81
Autor:
Akashdeep Bhardwaj, Ankit Vishnoi, Salil Bharany, Abdelzahir Abdelmaboud, Ashraf Osman Ibrahim, Mohamed Mamoun, Wamda Nagmeldin
Publikováno v:
PeerJ Computer Science, Vol 9, p e1771 (2023)
The Internet of Things has a bootloader and applications responsible for initializing the device’s hardware and loading the operating system or firmware. Ensuring the security of the bootloader is crucial to protect against malicious firmware or so
Externí odkaz:
https://doaj.org/article/781891cf3d624fc29cbd2a5aafbb49be