Towards efficient full 8-bit integer DNN online training on resource-limited devices without batch normalization

66 lower in power, 13 faster in the processing speed compared with the traditional 32-bit floating-point BN in the inference process. What’s more, the design of deep learning chips can be profoundly simplified in the absence of unfriendly square root operations in BN. Beyond this, EOQ has been evidenced to be more advantageous in small-batch online training with fewer batch samples. In summary, the EOQ framework is specially designed for reducing the high cost of convolution and BN in network training, demonstrating a broad application prospect of online training in resource-limited devices.