Engineers at the University of Florida have built a photonic chip that performs convolutions, the most compute-heavy operation in modern AI, using light instead of electricity and delivering roughly ...

* Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved. * SPDX-License-Identifier: Apache-2.0 * Licensed under the Apache License, Version 2.0 ...

The esp-nn optimized convolution functions are producing incorrect outputs, leading to a significant drop in model accuracy from 92% to below 70%. When using the standard ANSI C implementation, the ...

With the rapid development of machine learning, Deep Neural Network (DNN) exhibits superior performance in solving complex problems like computer vision and natural language processing compared with ...

Abstract: Recent advances in deep learning have driven significant success in synthetic aperture radar (SAR) automatic target recognition, particularly through convolutional neural network (CNN) based ...

Convolution is used in a variety of signal-processing applications, including time-domain-waveform filtering. In a recent series on the inverse fast Fourier transform (FFT), we concluded with a ...

Abstract: This work investigates the problem of efficiently learning discriminative low-dimensional (LD) representations of multiclass image objects. We propose a generic end-to-end approach that ...

Aiming at the problems of traditional image super-resolution reconstruction algorithms in the image reconstruction process, such as small receptive field, insufficient multi-scale feature extraction, ...