ELC-ECG : Efficient LSTM cell for ECG classification based on quantized architecture

• Long Short-Term Memory (LSTM) is one of the most popular and effective Recurrent Neural Network (RNN) models used for sequence learning in applications such as ECG signal classification. Complex LSTMs could hardly be deployed on resource-limited bio-medical wearable devices due to the huge amount of computations and memory requirements. Binary LSTMs are introduced to cope with this problem. However, naive binarization leads to significant accuracy loss in ECG classification. In this paper, we propose an efficient LSTM cell along with a novel hardware architecture for ECG classification. By deploying 5-level binarized inputs and just 1-level binarization for weights, output, and in-memory cell activations, the delay of one LSTM cell operation is reduced 50x with about 0.004% accuracy loss in comparison with full precision design of ECG classification.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences (hsv//eng)

Nyckelord

Electrocardiogram (ECG) Signal Classification

Long Short -Term Memory (LSTM)

Wearable Devices

Cells

Cytology

Electrocardiography

Memory architecture

Network architecture

Cell operation

Ecg classifications

Memory requirements

Novel hardware

Precision design

Recurrent neural network (RNN)

Sequence learning

Long short-term memory

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