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A Comparison of Evolvable Hardware Architectures for Classification Tasks
- In Proceedings 8th International Conference on Evolvable Systems (ICES
"... Abstract. We analyze and compare four different evolvable hardware approaches for classification tasks: An approach based on a programmable logic array architecture, an approach based on two-phase incremental evolution, a generic logic architecture with automatic definition of building blocks, and a ..."
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Abstract. We analyze and compare four different evolvable hardware approaches for classification tasks: An approach based on a programmable logic array architecture, an approach based on two-phase incremental evolution, a generic logic architecture with automatic definition of building blocks, and a specialized coarse-grained architecture with pre-defined building blocks. We base the comparison on a common data set and report on classification accuracy and training effort. The results show that classification accuracy can be increased by using modular, specialized classifier architectures. Furthermore, function level evolution, either with predefined functions derived from domain-specific knowledge or with functions that are automatically defined during evolution, also gives higher accuracy. Incremental and function level evolution reduce the search space and thus shortens the training effort. 1
Towards Multi-movement Hand Prostheses: Combining Adaptive Classification with High Precision Sockets
"... Abstract — The acceptance of hand prostheses strongly depends on their user-friendliness and functionality. Current prostheses are limited to a few movements and their operation is all but intuitive. The development of practically applicable multi-movement prostheses requires the combination of mode ..."
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Abstract — The acceptance of hand prostheses strongly depends on their user-friendliness and functionality. Current prostheses are limited to a few movements and their operation is all but intuitive. The development of practically applicable multi-movement prostheses requires the combination of modern classification methods with novel techniques for manufacturing high precision sockets. In this paper, we introduce an approach for classifying EMG signals taken from forearm muscles using support vector machines. This classifier technique is used in an adaptive operation mode and customized to the amputee, which allows us to recognize eleven different hand movements with high accuracy. Then, we present a novel manufacturing technique for prosthesis sockets enabling a precise amputee-specific fitting and EMG sensor placement. I.
Investigating Evolvable Hardware Classification for the BioSleeve Electromyographic Interface
"... Abstract—We investigate the applicability of an evolvable hardware classifier architecture for electromyography (EMG) data from the BioSleeve wearable human-machine interface, with the goal of having embedded training and classification. We investigate classification accuracy for datasets with 17 an ..."
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Abstract—We investigate the applicability of an evolvable hardware classifier architecture for electromyography (EMG) data from the BioSleeve wearable human-machine interface, with the goal of having embedded training and classification. We investigate classification accuracy for datasets with 17 and 11 gestures and compare to results of Support Vector Machines (SVM) and Random Forest classifiers. Classification accuracies are 91.5 % for 17 gestures and 94.4 % for 11 gestures. Initial results for a field programmable array (FPGA) implementation of the classifier architecture are reported, showing that the classifier architecture fits in a Xilinx XC6SLX45 FPGA. We also investigate a bagging-inspired approach for training the individual components of the classifier with a subset of the full training data. While showing some improvement in classification accuracy, it also proves useful for reducing the number of training
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"... Evolvable hardware (EHW) denotes the com-bination of evolutionary algorithms with re-configurable hardware technology to construct self-adaptive and self-optimizing hardware ..."
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Evolvable hardware (EHW) denotes the com-bination of evolutionary algorithms with re-configurable hardware technology to construct self-adaptive and self-optimizing hardware
Coping with Resource Fluctuations: The Run-time Reconfigurable Functional Unit Row Classifier Architecture
"... Abstract. The evolvable hardware paradigm facilitates the construc-tion of autonomous systems that can adapt to environmental changes and degrading effects in the computational resources. Extending these scenarios, we study the capability of evolvable hardware classifiers to adapt to intentional run ..."
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Abstract. The evolvable hardware paradigm facilitates the construc-tion of autonomous systems that can adapt to environmental changes and degrading effects in the computational resources. Extending these scenarios, we study the capability of evolvable hardware classifiers to adapt to intentional run-time fluctuations in the available resources, i.e., chip area, in this work. To that end, we leverage the Functional Unit Row (FUR) architecture, a coarse-grained reconfigurable classifier, and apply it to two medical benchmarks, the Pima and Thyroid data sets from the UCI Machine Learning Repository. We show that FUR’s clas-sification performance remains high during changes of the utilized chip area and that performance drops are quickly compensated for. Addition-ally, we demonstrate that FUR’s recovery capability benefits from extra resources. 1
