Hardware for DL Networks

This chapter proceeds to engage the previous section learning to empower the advanced hardware system knowledge that powers a sturdy performance to train the deep learning networks. A detailed hardware environment setting, configuration, and presentation are presented on various processing and computing kinds including AMD, POWER9, ARM, ARM \(+\) GPU, and X86 systems. These processing environments are showcased with an installation, setup, and configuration on edge servers. Deep learning needs high computing systems with customized configurations for various applications and tools, and hence, the book is not limited to deep learning tools and application, but as well educates users and professionals to know insights of what kinds of hardware and performance settings should be configured to achieve the best deep learning results. The book also provides sufficient QR coders to readers to quickly download all relevant tools, applications, and hardware configuration techniques in the need of the hour. Further, advanced installations like NVIDIA CUDA compiler, GPU hardware, GeForce multiprocessor, thread processing, IBM Watson CE, and large-scale AI business enterprise suite configuration are demonstrated in simple steps. At last, deployment of AI on X86 and Android phone is also presented. This chapter demonstrates and reveal the core insights of various AI hardware configurations including edge native AI hardware and in setting up Jetson NANO in IoT edge environment. The advanced deep learning deployments are discussed and illustrated with best examples of real-time deep learning applications.