Deep Neural Networks (DNNs), has seen revolutionary progress in recent years. Its applications spread from naïve image classification application to complex natural language processing like ChatGPT etc. Training of deep neural network (DNN) needs extensive use of hardware, time, and technical intelligence to suit specific application on a specific embedded processor. Therefore, trained DNN weights and network architecture are the intellectual property which needs to be protected from possible theft or abuse at the various stage of model development and deployment. Hence there is need of protection of Intellectual property of DNN and also there is need of identification of theft even if it happens in some case to claim the ownership of DNN weights. The Intellectual Property protection of DNN weights has attracted increasing serious attention in the academia and industries. Many works on IP protection for Deep Neural Networks (DNN) weights have been proposed. The vast majority of existing work uses naïve watermarking extraction to verify the ownership of the model after piracy occurs. In this paper a novel method for protection and identification for intellectual property related to DNN weights is presented. Our method is based on inserting the digital watermarks at learned least significant bits of weights for identification purpose and usages of hardware effuse for rightful usages of these watermarked weights on intended embedded processor.