This paper presents SensorNet which is a scalable and low-power embedded deep convolutional neural network (DCNN), designed to classify multimodal time series signals. Time series signals generated by different sensor modalities with different sampling rates are first converted to images (2-D signals), and then DCNN is utilized to automatically learn shared features in the images and perform the classification. SensorNet: 1) is scalable as it can process different types of time series data with variety of input channels and sampling rates; 2) does not need to employ separate signal processing techniques for processing the data generated by each sensor modality; 3) does not require expert knowledge for extracting features for each sensor data; 4) makes it easy and fast to adapt to new sensor modalities with a different sampling rate; 5) achieves very high detection accuracy for different case studies; and 6) has a very efficient architecture which makes it suitable to be deployed at Internet of Things and wearable devices. A custom low-power hardware architecture is also designed for the efficient deployment of SensorNet at embedded realtime systems. SensorNet performance is evaluated using three different case studies including physical activity monitoring, stand-alone tongue drive system, and stress detection, and it achieves an average detection accuracy of 98%, 96.2%, and 94% for each case study, respectively. We implement SensorNet using our custom hardware architecture on Xilinx FPGA (Artix-7) which on average consumes 246-μJ energy. To further reduce the power consumption, SensorNet is implemented using application-specified integrated circuit at the post layout level in 65-nm CMOS technology which consumes approximately 8x lower power compared to the FPGA implementation. In addition, SensorNet is implemented on NVIDIA Jetson TX2 SoC (CPU + GPU) and compared to TX2 single-core CPU and GPU implementations, FPGA-based SensorNet obtains 15× and 4× improvement in ...