Accelerating convolutional neural networks on FPGAs
نویسندگان
چکیده
منابع مشابه
Accelerating Convolutional Neural Network Systems
Convolutional Neural Networks have recently been shown to be highly effective classifiers for image and speech data. Due to the large volume of data required to build useful models, and the complexity of the models themselves, efficiency has become one of the primary concerns. This work shows that frequency domain methods can be utilised to accelerate the performance training, inference, and sl...
متن کاملAccelerating Deep Convolutional Neural Networks Using Specialized Hardware
Recent breakthroughs in the development of multi-layer convolutional neural networks have led to stateof-the-art improvements in the accuracy of non-trivial recognition tasks such as large-category image classification and automatic speech recognition [1]. These many-layered neural networks are large, complex, and require substantial computing resources to train and evaluate [2]. Unfortunately,...
متن کاملAccelerating Convolutional Neural Networks Using Low Precision Arithmetic
e recent trend in convolutional neural networks (CNN)[2] is to have deeper multilayered structures. While this improves the accuracy of the model, the amount of computation and the amount of data involved in learning and inference increases. In order to solve this problem, several techniques have been proposed to reduce the amount of data and the amount of computation by lowering the numerical...
متن کاملAccelerating Large-Scale Convolutional Neural Networks with Parallel Graphics Multiprocessors
Training convolutional neural networks (CNNs) on large sets of high-resolution images is too computationally intense to be performed on commodity CPUs. Such architectures however achieve state-of-the-art results on low-resolution machine vision tasks such as the recognition of handwritten characters. We have adapted the inherent multi-level parallelism of CNNs for Nvidia’s CUDA GPU architecture...
متن کاملAccelerating the Super-Resolution Convolutional Neural Network
As a successful deep model applied in image super-resolution (SR), the Super-Resolution Convolutional Neural Network (SRCNN) [1, 2] has demonstrated superior performance to the previous hand-crafted models either in speed and restoration quality. However, the high computational cost still hinders it from practical usage that demands real-time performance (24 fps). In this paper, we aim at accel...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SCIENTIA SINICA Informationis
سال: 2019
ISSN: 1674-7267
DOI: 10.1360/n112018-00291