The accurate electrocardiogram (ECG) interpretation is important for several potentially life-threatening cardiac diseases. Recently developed deep learning methods show their ability to distinguish some severe heart diseases. However, since deep neural network requires a high cost on memory consumption and computation, implementation scenarios of these interpretation methods are constrained to nonportable devices. Few commercial portable devices only have heartbeat detection ability, and therefore, only a few simple cardiac diseases can be diagnosed. In this article, to achieve diagnosing a wide range of cardiac diseases and continuous monitoring, a homecare-oriented ECG diagnosis platform is designed based on a large-scale multilabel deep conventional neural network. The accuracy of the proposed neural network model is guaranteed by our constructed large-scale ECG dataset, which is comprised of 206 468 standard 12-lead ECG recordings from 89 488 patients, with respect to 26 types of most common heart rhythms and conduction abnormalities. Meanwhile, targeting lightweight homecare or wearable applications, algorithm-hardware co-optimization is conducted to accelerate the model computation on an embedded platform with field-programmable gate array (FPGA) for continuous monitoring. Channel-level pruning and parameters quantization strategy are employed to optimize the network, and a reconfigurable accelerator hardware architecture is designed to accelerate the convolution computation on FPGA. The final quantified model achieved a promising score of 0.913 & x0025; and 86.7 & x0025; exact match ratio, in which parameters and floating-point operations per second (FLOPs) are significantly penalized compared to the original large-scale model. Real-time analysis is performed. Specifically, the average processing time for each ECG record is 2.895 s, and it can be applied to homecare or portable ECG diagnosis devices for continuous monitoring.