Design of Binary Convolution Operation Circuit for Binarized Neural Networks Using Single-Flux-Quantum Circuit

2022 ◽  
pp. 1-1
Author(s):  
Zongyuan Li ◽  
Yuki Yamanashi ◽  
Nobuyuki Yoshikawa
Keyword(s):  
Neural Networks ◽  
Quantum Circuit ◽  
Flux Quantum ◽  
Convolution Operation

scholarly journals Deep neural networks for quantum circuit mapping

2021 ◽  
Author(s):  
Giovanni Acampora ◽  
Roberto Schiattarella
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Quantum Algorithms ◽  
Quantum Algorithm ◽  
Quantum Circuit ◽  
Machine Learning Techniques ◽  
Quantum Computers ◽  
Physical Constraints ◽  
Proper Mapping ◽  
Correct Execution

AbstractQuantum computers have become reality thanks to the effort of some majors in developing innovative technologies that enable the usage of quantum effects in computation, so as to pave the way towards the design of efficient quantum algorithms to use in different applications domains, from finance and chemistry to artificial and computational intelligence. However, there are still some technological limitations that do not allow a correct design of quantum algorithms, compromising the achievement of the so-called quantum advantage. Specifically, a major limitation in the design of a quantum algorithm is related to its proper mapping to a specific quantum processor so that the underlying physical constraints are satisfied. This hard problem, known as circuit mapping, is a critical task to face in quantum world, and it needs to be efficiently addressed to allow quantum computers to work correctly and productively. In order to bridge above gap, this paper introduces a very first circuit mapping approach based on deep neural networks, which opens a completely new scenario in which the correct execution of quantum algorithms is supported by classical machine learning techniques. As shown in experimental section, the proposed approach speeds up current state-of-the-art mapping algorithms when used on 5-qubits IBM Q processors, maintaining suitable mapping accuracy.

scholarly journals Optimizing Convolution Neural Network on the TI C6678 multicore DSP

2018 ◽  
Vol 246 ◽  
pp. 03044 ◽  
Author(s):  
Guozhao Zeng ◽  
Xiao Hu ◽  
Yueyue Chen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Digital Signal Processor ◽  
High Performance ◽  
Digital Signal ◽  
Automatic Translation ◽  
Convolution Operation ◽  
Multicore Dsp

Convolutional Neural Networks (CNNs) have become the most advanced algorithms for deep learning. They are widely used in image processing, object detection and automatic translation. As the demand for CNNs continues to increase, the platforms on which they are deployed continue to expand. As an excellent low-power, high-performance, embedded solution, Digital Signal Processor (DSP) is used frequently in many key areas. This paper attempts to deploy the CNN to Texas Instruments (TI)’s TMS320C6678 multi-core DSP and optimize the main operations (convolution) to accommodate the DSP structure. The efficiency of the improved convolution operation has increased by tens of times.

Optimization of Single Flux Quantum Circuit Based Comparators Using PSO

2012 ◽  
Vol 26 (5) ◽  
pp. 1837-1841 ◽  
Author(s):  
Y. Tukel ◽  
A. Bozbey ◽  
C. Alp Tunc
Keyword(s):  
Quantum Circuit ◽  
Flux Quantum

Signal integrity in large-scale single-flux-quantum circuit

2006 ◽  
Vol 445-448 ◽  
pp. 1003-1007 ◽  
Author(s):  
Hirotaka Terai ◽  
Shinichi Yorozu ◽  
Akira Fujimaki ◽  
Nobuyuki Yoshikawa ◽  
Zhen Wang
Keyword(s):  
Large Scale ◽  
Signal Integrity ◽  
Quantum Circuit ◽  
Flux Quantum

Design of a Tiny Microprocessor Based on the Single-Flux-Quantum Circuit Technology

2002 ◽  
Author(s):  
N. Yoshikawa ◽  
F. Matsuzaki ◽  
N. Nakajima ◽  
K. Fujiwara ◽  
K. Yoda ◽  
...  
Keyword(s):  
Quantum Circuit ◽  
Flux Quantum ◽  
Circuit Technology

scholarly journals Adaptive Context Encoding Module for Semantic Segmentation

2020 ◽  
Vol 2020 (10) ◽  
pp. 27-1-27-7
Author(s):  
Congcong Wang ◽  
Faouzi Alaya Cheikh ◽  
Azeddine Beghdadi ◽  
Ole Jakob Elle
Keyword(s):  
Neural Networks ◽  
State Of The Art ◽  
Experimental Studies ◽  
Semantic Segmentation ◽  
Multiple Scale ◽  
Context Information ◽  
Convolution Operation ◽  
Sampling Locations ◽  
Spatial Pyramid Pooling ◽  
Spatial Pyramid

The object sizes in images are diverse, therefore, capturing multiple scale context information is essential for semantic segmentation. Existing context aggregation methods such as pyramid pooling module (PPM) and atrous spatial pyramid pooling (ASPP) employ different pooling size or atrous rate, such that multiple scale information is captured. However, the pooling sizes and atrous rates are chosen empirically. Rethinking of ASPP leads to our observation that learnable sampling locations of the convolution operation can endow the network learnable fieldof- view, thus the ability of capturing object context information adaptively. Following this observation, in this paper, we propose an adaptive context encoding (ACE) module based on deformable convolution operation where sampling locations of the convolution operation are learnable. Our ACE module can be embedded into other Convolutional Neural Networks (CNNs) easily for context aggregation. The effectiveness of the proposed module is demonstrated on Pascal-Context and ADE20K datasets. Although our proposed ACE only consists of three deformable convolution blocks, it outperforms PPM and ASPP in terms of mean Intersection of Union (mIoU) on both datasets. All the experimental studies confirm that our proposed module is effective compared to the state-of-the-art methods.

scholarly journals Multi-Channel Pooling Graph Neural Networks

2021 ◽  
Author(s):  
Jinlong Du ◽  
Senzhang Wang ◽  
Hao Miao ◽  
Jiaqiang Zhang
Keyword(s):  
Neural Networks ◽  
Local Structure ◽  
Superior Performance ◽  
Topology Structure ◽  
Graph Representations ◽  
Convolution Operation ◽  
Global Topology ◽  
Benchmark Datasets ◽  
Graph Neural Networks ◽  
Local Topology

Graph pooling is a critical operation to downsample a graph in graph neural networks. Existing coarsening pooling methods (e.g. DiffPool) mostly focus on capturing the global topology structure by assigning the nodes into several coarse clusters, while dropping pooling methods (e.g. SAGPool) try to preserve the local topology structure by selecting the top-k representative nodes. However, there lacks an effective method to integrate the two types of methods so that both the local and the global topology structure of a graph can be well captured. To address this issue, we propose a Multi-channel Graph Pooling method named MuchPool, which captures the local structure, the global structure, and node feature simultaneously in graph pooling. Specifically, we use two channels to conduct dropping pooling based on the local topology and node features respectively, and one channel to conduct coarsening pooling. Then a cross-channel convolution operation is designed to refine the graph representations of different channels. Finally, the pooling results are aggregated as the final pooled graph. Extensive experiments on six benchmark datasets present the superior performance of MuchPool. The code of this work is publicly available at Github.

Sign in / Sign up

Export Citation Format

Share Document