  Nowadays a diverse range of physiological data can be captured continuously
for various applications in particular wellbeing and healthcare. Such data
require efficient methods for classification and analysis. Deep learning
algorithms have shown remarkable potential regarding such analyses, however,
the use of these algorithms on low-power wearable devices is challenged by
resource constraints such as area and power consumption. Most of the available
on-chip deep learning processors contain complex and dense hardware
architectures in order to achieve the highest possible throughput. Such a trend
in hardware design may not be efficient in applications where on-node
computation is required and the focus is more on the area and power efficiency
as in the case of portable and embedded biomedical devices. This paper presents
an efficient time-series classifier capable of automatically detecting
effective features and classifying the input signals in real-time. In the
proposed classifier, throughput is traded off with hardware complexity and cost
using resource sharing techniques. A Convolutional Neural Network (CNN) is
employed to extract input features and then a Long-Short-Term-Memory (LSTM)
architecture with ternary weight precision classifies the input signals
according to the extracted features. Hardware implementation on a Xilinx FPGA
confirm that the proposed hardware can accurately classify multiple complex
biomedical time series data with low area and power consumption and outperform
all previously presented state-of-the-art records. Most notably, our classifier
reaches 1.3$\times$ higher GOPs/Slice than similar state of the art FPGA-based
accelerators.
