High-accuracy optical convolution unit architecture for convolutional neural networks by cascaded acousto-optical modulator arrays
Optical neural networks (ONNs) have become competitive candidates for the next
generation of high-performance neural network accelerators because of their low power …
generation of high-performance neural network accelerators because of their low power …
Optimization of the Brillouin instantaneous frequency measurement using convolutional neural networks
The Brillouin instantaneous frequency measurement (B-IFM) is used to measure
instantaneous frequencies of an arbitrary signal with high frequency and broad bandwidth …
instantaneous frequencies of an arbitrary signal with high frequency and broad bandwidth …
Artificial Intelligence applications in Noise Radar Technology
AL Sénica, PAC Marques… - IET Radar, Sonar & …, 2024 - Wiley Online Library
Radar systems are a topic of great interest, especially due to their extensive range of
applications and ability to operate in all weather conditions. Modern radars have high …
applications and ability to operate in all weather conditions. Modern radars have high …
High-speed multi-layer convolutional neural network based on free-space optics
H Sadeghzadeh, S Koohi - IEEE Photonics Journal, 2022 - ieeexplore.ieee.org
Convolutional neural networks (CNNs) are at the heart of several machine learning
applications, while they suffer from computational complexity due to their large number of …
applications, while they suffer from computational complexity due to their large number of …
TinyEP: TinyML-enhanced Energy Profiling for Extreme Edge Devices
K Müller, J Weidner, N Franchi, P Wägemann - IEEE Access, 2024 - ieeexplore.ieee.org
The widespread integration of the Internet of Things (IoT) into daily operations has made
optimizing energy consumption in low-power edge devices increasingly important. This is …
optimizing energy consumption in low-power edge devices increasingly important. This is …
Fault tolerance and noise immunity in freespace diffractive optical neural networks
Free-space diffractive optical networks are a class of trainable optical media that are
currently being explored as a novel hardware platform for neural engines. The training …
currently being explored as a novel hardware platform for neural engines. The training …
Low-Complexity Frequency-Dependent Linearizers Based on Parallel Bias-Modulus and Bias-ReLU Operations
DR Linares, H Johansson - arXiv preprint arXiv:2412.16210, 2024 - arxiv.org
This paper introduces low-complexity frequency-dependent (memory) linearizers designed
to suppress nonlinear distortion in analog-to-digital interfaces. Two different linearizers are …
to suppress nonlinear distortion in analog-to-digital interfaces. Two different linearizers are …
[PDF][PDF] 一种基于神经网络的THD 参数快速估计算法
侯琳杰， 解维坤， 陈世博， 刘雨涛， 赵贻玖 - 仪器仪表学报, 2023 - emt.cnjournals.com
模数转换器(ADC) 测试主要包括静态参数和动态参数两个测试过程. 随着性能的提升, ADC
的测试复杂度和成本也急剧增加. 替代测试, 即通过分析两类参数间的关系来实现一个测试过程 …
的测试复杂度和成本也急剧增加. 替代测试, 即通过分析两类参数间的关系来实现一个测试过程 …
Deep analog-to-digital converter for wireless communication
With the advent of the 5G wireless networks, achieving tens of gigabits per second
throughputs and low, milliseconds, latency has become a reality. This level of performance …
throughputs and low, milliseconds, latency has become a reality. This level of performance …
Training a machine learning system for ADC compensation
R van Veldhoven - US Patent 11,101,810, 2021 - Google Patents
Analog to digital conversion errors caused by non-linearities or other sources of distortion in
an analog-to-digital converter are compensated for by use of a machine learning system …
an analog-to-digital converter are compensated for by use of a machine learning system …