CNN acceleration algorithms, including Winograd, Fast Fourier Transform (FFT) and
Number Theoretic transform (NTT), have demonstrated their potential in efficiently operating …

Exploding development of convolutional neural network (CNN) benefits greatly from the
hardware-based acceleration to maintain low latency and high utilization of resources. To …

Convolutional Neural Networks (CNNs) are crucial in various applications, but their
deployment on resource-constrained edge devices poses challenges. This study presents …

In the realm of neural network computation, optical neural network accelerators (ONNs)
have emerged as a promising solution, leveraging the inherent speed and parallelism of …

Inspired by the key operation of vision transformers (ViTs), convolutional neural networks
(CNNs) have widely adopted arbitrary-kernel convolutions to achieve high performance in …

Fast convolution algorithms, including Winograd and FFT, can efficiently accelerate
convolution operations in deep models. However, these algorithms depend on high …

By introducing the Fermat number transform into chromatic dispersion compensation and
adaptive equalization, the computational complexity has been reduced by 68% compared …

Edge computing is a computing framework that offers fewer computing resources compared
to cloud computing but brings enterprise applications closer to data sources like Internet of …

This paper introduces FFT1d-Conv, a method to accelerate convolution operations in
convolutional neural networks. Convolution is one of the most computationally intensive …

Fast convolution algorithms like Winograd and the Fourier transform are well-known for their
substantial reduction in the multiplication complexity of Convolutional Neural Networks …