A Convolutional Neural Network VLSI for Image Recognition Using Merged/Mixed Analog-Digital Architecture

Korekado, Keisuke; Matsukawa, Takashi; Nakamura, Osamu; Ando, Hiroshi; Nakano, T.; Matsugu, Masakazu; Iwata, Atsushi

doi:10.1007/978-3-540-45226-3_24

Cited by 25 publications

(15 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep Learning (DL) approaches are based on the principle of using ANNs with multiple hidden layers, where training is both unsupervised (bottom-up) to generate higher level representations of sensory data which can then be used for training a classifier (top down) based on standard supervised training algorithms (Hinton & Salakhutdinov, 2006). Feature learning methods are based on supervised approaches such as Deep NNs, Convolutional NNs and Recurrent NNs along with unsupervised techniques such as Deep Belief Networks and Convolutional Neural Networks (CNNs) and provide deep architecture that combine structural elements of local receptive fields, shared weights, and pooling that aims to imitate the processing of simple and complex cortical cells found in animal vision systems (Korekado et al, 2003).…”

Section: Computational Intelligence For Big Data Analyticsmentioning

confidence: 99%

TEMPORARY REMOVAL: Big Data analytics: Computational intelligence techniques and application areas

Iqbal

Doctor

et al. 2016

International Journal of Information Management

View full text Add to dashboard Cite

a b s t r a c tBig Data has a significant impact in modern society. In this paper we investigated the importance of Big Data in modern life, and in terms of the economy, and discussed the challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explored the potential of the powerful combination of Big Data and Computational intelligence and identified a number of areas where novel applications in real world problems can be developed by utilizing these powerful tools and technologies. We presented a novel data modelling methodology which introduces a novel biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). In this paper, we have also discussed various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment.

show abstract

Section: Computational Intelligence For Big Data Analyticsmentioning

confidence: 99%

TEMPORARY REMOVAL: Big Data analytics: Computational intelligence techniques and application areas

Iqbal

Doctor

et al. 2016

International Journal of Information Management

View full text Add to dashboard Cite

show abstract

“…Deep Learning (DL) approaches are based on the principle of using ANNs with multiple hidden layers, where training is both unsupervised (bottom-up) to generate higher level representations of sensory data which can then be used for training a classifier (top down) based on standard supervised training algorithms (Hinton and Salakhutdinov, 2006). Feature learning methods are based on supervised approaches such as Deep NNs, Convolutional NNs and Recurrent NNs along with unsupervised techniques such as Deep Belief Networks and Convolutional Neural Networks (CNNs) provide deep architecture that combine structural elements of local receptive fields, shared weights, and pooling that aim to imitate the processing of simple and complex cortical cells found in animal vision systems (Korekado et al, 2003).…”

Section: Computational Intelligence For Big Data Analyticsmentioning

confidence: 99%

Big data analytics: Computational intelligence techniques and application areas

Iqbal

Doctor

et al. 2020

Technological Forecasting and Social Change

168

View full text Add to dashboard Cite

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment.

show abstract

“…One of the first CNN implementations on hardware dates back to the early 90's where an ANNA chip (a mixed analog/digital neural-network chip) was used to implement a CNN [33][34][35]. Korekado et al [36] proposed a VLSI architecture of high performance and low power was used to implement a CNN. This architecture uses a hybrid approach composed of pulse-width modulation (PWM) and a digital circuitry.…”

Section: Convolution Neural Network Backgroudmentioning

confidence: 99%

Implementation Strategy of Convolution Neural Networks on Field Programmable Gate Arrays for Appliance Classification Using the Voltage and Current (V-I) Trajectory

Baptista

Mostafa

Pereira³

et al. 2018

Energies

View full text Add to dashboard Cite

Specific information about types of appliances and their use in a specific time window could help determining in details the electrical energy consumption information. However, conventional main power meters fail to provide any specific information. One of the best ways to solve these problems is through non-intrusive load monitoring, which is cheaper and easier to implement than other methods. However, developing a classifier for deducing what kind of appliances are used at home is a difficult assignment, because the system should identify the appliance as fast as possible with a higher degree of certainty. To achieve all these requirements, a convolution neural network implemented on hardware was used to identify the appliance through the voltage and current (V-I) trajectory. For the implementation on hardware, a field programmable gate array (FPGA) was used to exploit processing parallelism in order to achieve optimal performance. To validate the design, a publicly available Plug Load Appliance Identification Dataset (PLAID), constituted by 11 different appliances, has been used. The overall average F-score achieved using this classifier is 78.16% for the PLAID 1 dataset. The convolution neural network implemented on hardware has a processing time of approximately 5.7 ms and a power consumption of 1.868 W.

show abstract

A Convolutional Neural Network VLSI for Image Recognition Using Merged/Mixed Analog-Digital Architecture

Cited by 25 publications

References 4 publications

TEMPORARY REMOVAL: Big Data analytics: Computational intelligence techniques and application areas

TEMPORARY REMOVAL: Big Data analytics: Computational intelligence techniques and application areas

Big data analytics: Computational intelligence techniques and application areas

Implementation Strategy of Convolution Neural Networks on Field Programmable Gate Arrays for Appliance Classification Using the Voltage and Current (V-I) Trajectory

Contact Info

Product

Resources

About