Results 51 to 60 of about 1,050,120 (328)
An Efficient Hardware Accelerator for Structured Sparse Convolutional Neural Networks on FPGAs [PDF]
IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2020 Deep convolutional neural networks (CNNs) have achieved state-of-the-art performance in a wide range of applications. However, deeper CNN models, which are usually computation consuming, are widely required for complex artificial intelligence (AI) tasks.
Chaoyang Zhu +5 more
semanticscholar +1 more source
Dynamic accelerator provisioning for SSH tunnels in NFV environments [PDF]
, 2019 In this demonstration, we present dynamic allocation of accelerator resources to SSH tunnels in an NFV environment. In order to accelerate a VNF, its compute-intensive operations are offloaded to hardware cores running on an FPGA.
Colle, Didier +3 more
core +1 more source
Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator [PDF]
Design Automation Conference, 2020 Neural architecture search (NAS) has been very successful at outperforming human-designed convolutional neural networks (CNN) in accuracy, and when hardware information is present, latency as well.
Mohamed S. Abdelfattah +5 more
semanticscholar +1 more source
CPU-Accelerator Co-Scheduling for CNN Acceleration at the Edge
IEEE Access, 2020 Convolutional neural networks (CNNs) are widely deployed for many artificial intelligence (AI) applications, such as object detection and image classification. Due to the burgeoning revolution in edge AI, CNN hardware accelerators are also being employed
Yeongmin Kim, Joonho Kong, Arslan Munir
doaj +1 more source
A High-Efficiency FPGA-Based Multimode SHA-2 Accelerator
IEEE Access, 2022 The secure hash algorithm 2 (SHA-2) family, including the SHA-224/256/384/512 hash functions, is widely adopted in many modern domains, ranging from Internet of Things devices to cryptocurrency.
Hoai Luan Pham +3 more
doaj +1 more source
Electronics, 2021 Machine learning is becoming the cornerstones of smart and autonomous systems. Machine learning algorithms can be categorized into supervised learning (classification) and unsupervised learning (clustering).
Srikanth Ramadurgam, Darshika G. Perera
semanticscholar +1 more source
A Partial Method for Calculating CNN Networks Based On Loop Tiling [PDF]
International Journal of Information and Communication Technology Research, 2023 Convolutional Neural Networks (CNNs) have been widely deployed in the fields of artificial intelligence and computer vision. In these applications, the CNN part is the most computationally intensive.
Ali Ali A.D. Farahani +3 more
doaj
Remote Sensing, 2022 In recent years, convolutional neural network (CNN)-based algorithms have been widely used in remote sensing image processing and show tremendous performance in a variety of application fields.
Tianwei Yan +4 more
doaj +1 more source
, 2023 AbstractWith Moore’s law and Dennard’s scaling no longer fueling the improvement in computing performance, new avenues for increasing performance are needed. Hardware acceleration is one avenue where many researchers and industrial parties are working and investing.
openaire +1 more source
Large Field-Size Throughput/Area Accelerator for Elliptic-Curve Point Multiplication on FPGA
Applied Sciences, 2023 This article presents a throughput/area accelerator for elliptic-curve point multiplication over GF(2571). To optimize the throughput, we proposed an efficient hardware accelerator architecture for a fully recursive Karatsuba multiplier to perform ...
Ahmed Alhomoud +5 more
doaj +1 more source