Abstract: In this work, we propose a multiplication-less deep convolution neural network, called BD-NET. As far as we know, BD-NET is the first to use binarized depthwise separable convolution block ...

High-performance matrix multiplication remains a cornerstone of numerical computing, underpinning a wide array of applications from scientific simulations to machine learning. Researchers continually ...

Abstract: Convolution neural network (CNN) widely used for the application of computer vision, such applications would be beneficial for CNN if their workload could be reduced. In this paper, we ...

High-performance Triton-based GPU kernels for accelerating core deep learning operations, from matrix multiplication to convolutions and activation functions. Modern deep learning frameworks rely on ...

A high-performance hardware accelerator designed for image convolution operations in Convolutional Neural Networks (CNNs). This implementation provides an efficient FPGA-based solution for ...