A low-power asynchronous hardware implementation of a novel SVM classifier, with an application in a speech recognition system

Batista, Gracieth C.; Oliveira, Duarte L.; Saotome, Osamu; Silva, Washington Luis Santos

doi:10.1016/j.mejo.2020.104907

Cited by 13 publications

(18 citation statements)

References 7 publications

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“…Kachris C. et al [ 28 ] implemented a Logistic Regression architecture, which was incorporated into a framework for data analytics called Spark. Batista G. et al [ 29 ] proposed an SVM classifier for speech recognition. Wu R. et al [ 30 ] presented an SVM architecture for matrix computing with changeable dimensions.…”

Section: Resultsmentioning

confidence: 99%

“…Their accuracy results correspond to these datasets. Novickis R. et al [ 27 ] used the Mean Absolute Error metric to evaluate three different FFNNs configurations; Kachris C. et al [ 28 ] and Batista G. et al [ 29 ] evaluated their works using the accuracy metric; Wu R. et al [ 30 ] chose the effective utilization rate for evaluation.…”

Section: Resultsmentioning

confidence: 99%

“…Batista G. et al [ 29 ] proposed an asynchronous non-linear pipeline architecture of SVM classifier for speech recognition of 30 different classes aiming at low-power applications using a Gaussian kernel function. The architecture consists of four stages with a Multiply-Accumulator unit application and three different control circuits.…”

Section: Related Workmentioning

confidence: 99%

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Trade-Off Analysis of Hardware Architectures for Channel-Quality Classification Models

Torres-Alvarado

Morales-Rosales

Algredo‐Badillo

et al. 2022

Sensors

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The latest generation of communication networks, such as SDVN (Software-defined vehicular network) and VANETs (Vehicular ad-hoc networks), should evaluate their communication channels to adapt their behavior. The quality of the communication in data networks depends on the behavior of the transmission channel selected to send the information. Transmission channels can be affected by diverse problems ranging from physical phenomena (e.g., weather, cosmic rays) to interference or faults inherent to data spectra. In particular, if the channel has a good transmission quality, we might maximize the bandwidth use. Otherwise, although fault-tolerant schemes degrade the transmission speed by solving errors or failures should be included, these schemes spend more energy and are slower due to requesting lost packets (recovery). In this sense, one of the open problems in communications is how to design and implement an efficient and low-power-consumption mechanism capable of sensing the quality of the channel and automatically making the adjustments to select the channel over which transmit. In this work, we present a trade-off analysis based on hardware implementation to identify if a channel has a low or high quality, implementing four machine learning algorithms: Decision Trees, Multi-Layer Perceptron, Logistic Regression, and Support Vector Machines. We obtained the best trade-off with an accuracy of 95.01% and efficiency of 9.83 Mbps/LUT (LookUp Table) with a hardware implementation of a Decision Tree algorithm with a depth of five.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Trade-Off Analysis of Hardware Architectures for Channel-Quality Classification Models

Torres-Alvarado

Morales-Rosales

Algredo‐Badillo

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Over 10× computing speed and 200× power-delay are obtained as compared with ARM A53. Gracieth et al [16] proposed a 4-stage, low-power SVM pipeline architecture capable of achieving 98% of accuracy on over 30 classification tasks. It consumes only 1315 LUTs of resources and operates at a system frequency of 50 MHz.…”

Section: Related Workmentioning

confidence: 99%

MLoF: Machine Learning Accelerators for the Low-Cost FPGA Platforms

Ruiqi¹,

Zheng

et al. 2021

Applied Sciences

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In Internet of Things (IoT) scenarios, it is challenging to deploy Machine Learning (ML) algorithms on low-cost Field Programmable Gate Arrays (FPGAs) in a real-time, cost-efficient, and high-performance way. This paper introduces Machine Learning on FPGA (MLoF), a series of ML IP cores implemented on the low-cost FPGA platforms, aiming at helping more IoT developers to achieve comprehensive performance in various tasks. With Verilog, we deploy and accelerate Artificial Neural Networks (ANNs), Decision Trees (DTs), K-Nearest Neighbors (k-NNs), and Support Vector Machines (SVMs) on 10 different FPGA development boards from seven producers. Additionally, we analyze and evaluate our design with six datasets, and compare the best-performing FPGAs with traditional SoC-based systems including NVIDIA Jetson Nano, Raspberry Pi 3B+, and STM32L476 Nucle. The results show that Lattice’s ICE40UP5 achieves the best overall performance with low power consumption, on which MLoF averagely reduces power by 891% and increases performance by 9 times. Moreover, its cost, power, Latency Production (CPLP) outperforms SoC-based systems by 25 times, which demonstrates the significance of MLoF in endpoint deployment of ML algorithms. Furthermore, we make all of the code open-source in order to promote future research.

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“…Dey et al [ 23 ] studied performance of SVM classification algorithms running on embedded processors and suggested parallel computations. There are also examples of SVMs implemented to FPGA-based systems, like speech recognition system [ 24 ] or melanoma detection [ 25 ]. Regarding ANFIS, there are many examples of real-time applications of these algorithms in automatic control area, like of hydraulic systems [ 26 ], active suspension system for passenger comfort improvement [ 27 ], robot arms [ 28 ], or mobile robots navigation for target seeking and obstacle avoidance [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning Algorithms for Structure State Prediction in Operational Load Monitoring

Mucha

2020

Sensors

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The aim of operational load monitoring is to make predictions about the remaining usability time of structures, which is extremely useful in aerospace industry where in-service life of aircraft structural components can be maximized, taking into account safety. In order to make such predictions, strain sensors are mounted to the structure, from which data are acquired during operational time. This allows to determine how many load cycles has the structure withstood so far. Continuous monitoring of the strain distribution of the whole structure can be complicated due to vicissitude nature of the loads. Sensors should be mounted in places where stress and strain accumulations occur, and due to experiencing variable loads, the number of required sensors may be high. In this work, different machine learning and artificial intelligence algorithms are implemented to predict the current safety factor of the structure in its most stressed point, based on relatively low number of strain measurements. Adaptive neuro-fuzzy inference systems (ANFIS), support-vector machines (SVM) and Gaussian processes for machine learning (GPML) are trained with simulation data, and their effectiveness is measured using data obtained from experiments. The proposed methods are compared to the earlier work where artificial neural networks (ANN) were proven to be efficiently used for reduction of the number of sensors in operational load monitoring processes. A numerical comparison of accuracy and computational time (taking into account possible real-time applications) between all considered methods is provided.

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A low-power asynchronous hardware implementation of a novel SVM classifier, with an application in a speech recognition system

Cited by 13 publications

References 7 publications

Trade-Off Analysis of Hardware Architectures for Channel-Quality Classification Models

Trade-Off Analysis of Hardware Architectures for Channel-Quality Classification Models

MLoF: Machine Learning Accelerators for the Low-Cost FPGA Platforms

Comparison of Machine Learning Algorithms for Structure State Prediction in Operational Load Monitoring

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