Comput. Biol. Med. - Machine learning on-a-chip: a high-performance low-power reusable neuron architecture for artificial neural networks in ECG classifications.

Tópicos

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Resumo

Artificial neural networks (ANNs) are a promising machine learning technique in classifying non-linear electrocardiogram (ECG) signals and recognizing abnormal patterns suggesting risks of cardiovascular diseases (CVDs). In this paper, we propose a new reusable neuron architecture (RNA) enabling a performance-efficient and cost-effective silicon implementation for ANN. The RNA architecture consists of a single layer of physical RNA neurons, each of which is designed to use minimal hardware resource (e.g., a single 2-input multiplier-accumulator is used to compute the dot product of two vectors). By carefully applying the principal of time sharing, RNA can multiplexs this single layer of physical neurons to efficiently execute both feed-forward and back-propagation computations of an ANN while conserving the area and reducing the power dissipation of the silicon. A three-layer 51-30-12 ANN is implemented in RNA to perform the ECG classification for CVD detection. This RNA hardware also allows on-chip automatic training update. A quantitative design space exploration in area, power dissipation, and execution speed between RNA and three other implementations representative of different reusable hardware strategies is presented and discussed. Compared with an equivalent software implementation in C executed on an embedded microprocessor, the RNA ASIC achieves three orders of magnitude improvements in both the execution speed and the energy efficiency.

Resumo Limpo

artifici neural network ann promis machin learn techniqu classifi nonlinear electrocardiogram ecg signal recogn abnorm pattern suggest risk cardiovascular diseas cvds paper propos new reusabl neuron architectur rna enabl performanceeffici costeffect silicon implement ann rna architectur consist singl layer physic rna neuron design use minim hardwar resourc eg singl input multiplieraccumul use comput dot product two vector care appli princip time share rna can multiplex singl layer physic neuron effici execut feedforward backpropag comput ann conserv area reduc power dissip silicon threelay ann implement rna perform ecg classif cvd detect rna hardwar also allow onchip automat train updat quantit design space explor area power dissip execut speed rna three implement repres differ reusabl hardwar strategi present discuss compar equival softwar implement c execut embed microprocessor rna asic achiev three order magnitud improv execut speed energi effici

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