Cellular neural network friendly convolutional neural networks — CNNs with CNNs

Horváth, András; Hillmer, Michael; Lou, Qiuwen; Hu, Xiaobo Sharon; Niemier, Michael

doi:10.23919/date.2017.7926973

Cited by 21 publications

(17 citation statements)

References 18 publications

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“…Classification algorithms are an area of machine vision that has experienced tremendous growth over the past decade with the development of convolutional neural networks (CNNs), a form of artificial intelligence that is loosely based on the neural architecture of animal visual systems [ 6 , 7 ]. For a description of CNNs and recent advances in machine vision the reader is directed to review articles on this topic [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

A High-Throughput Phenotyping System Using Machine Vision to Quantify Severity of Grapevine Powdery Mildew

et al. 2019

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Powdery mildews present specific challenges to phenotyping systems that are based on imaging. Having previously developed low-throughput, quantitative microscopy approaches for phenotyping resistance to Erysiphe necator on thousands of grape leaf disk samples for genetic analysis, here we developed automated imaging and analysis methods for E. necator severity on leaf disks. By pairing a 46-megapixel CMOS sensor camera, a long-working distance lens providing 3.5× magnification, X-Y sample positioning, and Z-axis focusing movement, the system captured 78% of the area of a 1-cm diameter leaf disk in 3 to 10 focus-stacked images within 13.5 to 26 seconds. Each image pixel represented 1.44 μm2 of the leaf disk. A convolutional neural network (CNN) based on GoogLeNet determined the presence or absence of E. necator hyphae in approximately 800 subimages per leaf disk as an assessment of severity, with a training validation accuracy of 94.3%. For an independent image set the CNN was in agreement with human experts for 89.3% to 91.7% of subimages. This live-imaging approach was nondestructive, and a repeated measures time course of infection showed differentiation among susceptible, moderate, and resistant samples. Processing over one thousand samples per day with good accuracy, the system can assess host resistance, chemical or biological efficacy, or other phenotypic responses of grapevine to E. necator. In addition, new CNNs could be readily developed for phenotyping within diverse pathosystems or for diverse traits amenable to leaf disk assays.

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Section: Introductionmentioning

confidence: 99%

A High-Throughput Phenotyping System Using Machine Vision to Quantify Severity of Grapevine Powdery Mildew

et al. 2019

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“…1) is the summation of the output capacitance of nearby OTAs. e delay and energy estimation of a CeNN cell in this paper is di erent from that in [19] in that: (1) 32 nm technology is used for the hardware design, (2) the m s of the OTAs are larger for faster processing while still satisfying a given power requirement, and (3) the cell resistance R cell in [19] is assumed to be the absolute value of the sum of m s, which leads to much larger se ling times. erefore, the work in [19] is a conservative estimation and overestimates the delay and the energy.…”

Section: Cenn Cells Designmentioning

confidence: 88%

“…A CeNN architecture is a spatially invariant, M × N array of identical cells ( Fig. 1a) [19]. Each cell C i j has identical connections with adjacent cells in a prede ned neighborhood.…”

Section: Cenn Basicsmentioning

confidence: 99%

A Mixed Signal Architecture for Convolutional Neural Networks

Lou

Pan²,

McGuinness

et al. 2019

J. Emerg. Technol. Comput. Syst.

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Deep neural network (DNN) accelerators with improved energy and delay are desirable for meeting the requirements of hardware targeted for IoT and edge computing systems. Convolutional neural networks (CoNNs) belong to one of the most popular types of DNN architectures. is paper presents the design and evaluation of an accelerator for CoNNs. e system-level architecture is based on mixed-signal, cellular neural networks (CeNNs). Speci cally, we present (i) the implementation of di erent layers, including convolution, ReLU, and pooling, in a CoNN using CeNN, (ii) modi ed CoNN structures with CeNN-friendly layers to reduce computational overheads typically associated with a CoNN, (iii) a mixed-signal CeNN architecture that performs CoNN computations in the analog and mixed signal domain, and (iv) design space exploration that identi es what CeNN-based algorithm and architectural features fare best compared to existing algorithms and architectures when evaluated over common datasets -MNIST and CIFAR-10. Notably, the proposed approach can lead to 8.7× improvements in energy-delay product (EDP) per digit classi cation for the MNIST dataset at iso-accuracy when compared with the state-of-the-art DNN engine, while our approach could o er 4.3× improvements in EDP when compared to other network implementations for the CIFAR-10 dataset.When considering application-speci c hardware to support neural networks, it is important that said hardware can implement networks that can be extensible to a large class of networks, and solve a large collection of application-level problems. Deep neural networks (DNNs) represent a class of such networks and have demonstrated their strength in applications such as playing the game of Go [54], image and video analysis [32], target tracking [31], etc. In this paper, we use convolutional neural network (CoNN) as a case study for DNNs due to its general prevalence. CoNNs are computationally intensive, which could lead to high latency and energy for inference and even higher latency/energy for training. e focus of this paper is on developing a low energy/delay mixed-signal system based on cellular neural networks (CeNNs) for realizing CoNN.A Cellular Nonlinear/Neural Network (CeNN) is an analog computing architecture [11] that could be well suited for many information processing tasks. In a CeNN, identical processing units (called cells) process analog information in a concurrent manner. Interconnection between cells is typically local (i.e., nearest neighbor) and space-invariant. For spatio-temporal applications, CeNNs can o er vastly superior performance and power e ciency when compared to conventional von Neumann architectures [47,61]. Using "CeNNs for CoNN" allows the bulk of the computation associated with a CoNN to be performed in the analog domain. Sensed information could immediately be processed with no analog-to-digital conversion (ADC). Also, inference-based processing tasks can tolerate lower precision (e.g., Google's TPU employs 8-bit integer matrix multiplies [24]) typically associa...

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“…e authors [21][22][23][24][25][26][27] performed for the CNN processor with low hardware configuration for image processing. In [21], real-time requirements for video processing applications are fully satisfied that allows early segmentation to be used and efficient preprocessing technique to perform sophisticated routines for configuration.…”

Section: Discussionmentioning

confidence: 99%

Hand Gesture Recognition Algorithm Using SVM and HOG Model for Control of Robotic System

Huu

Ngoc

2021

Journal of Robotics

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In this study, we propose the gesture recognition algorithm using support vector machines (SVM) and histogram of oriented gradient (HOG). Besides, we also use the CNN model to classify gestures. We approach and select techniques of applying problem controlling for the robotic system. The goal of the algorithm is to detect gestures with real-time processing speed, minimize interference, and reduce the ability to capture unintentional gestures. Static gesture controls are used in this study including on, off, increasing, and decreasing. Besides, it uses motion gestures including turning on the status switch and increasing and decreasing the volume. Results show that the algorithm is up to 99% accuracy with a 70-millisecond execution time per frame that is suitable for industrial applications.

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Cellular neural network friendly convolutional neural networks — CNNs with CNNs

Cited by 21 publications

References 18 publications

A High-Throughput Phenotyping System Using Machine Vision to Quantify Severity of Grapevine Powdery Mildew

A High-Throughput Phenotyping System Using Machine Vision to Quantify Severity of Grapevine Powdery Mildew

A Mixed Signal Architecture for Convolutional Neural Networks

Hand Gesture Recognition Algorithm Using SVM and HOG Model for Control of Robotic System

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