Artificial Intelligence for Network Engineers: Introduction

Several books on artificial intelligence (AI) and deep learning (DL) have been published over the past decade. However, I have yet to find a book that explains deep learning from a networking perspective while providing a solid introduction to DL. My goal is to fill this gap by writing a book titled AI for Network Engineers (note that the title name may change during the writing process). Writing about such a complex subject will take time, but I hope to complete and release it within a year.

Part I: Deep Learning and Deep Neural Networks

The first part of the book covers the theory behind Deep Learning. It begins by explaining the construct of a single artificial neuron and its functionality. Then, it explores various Deep Neural Network models, such as Feedforward Neural Networks (FNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). Next, the first part discusses data and model parallelization strategies such as Data, Pipeline, and Tensor Parallelism, explaining how input data and/or model sizes that exceed the memory capacity of GPUs within a single server can be distributed across multiple GPU servers.

Part II: AI Data Center Networking - Lossless Ethernet

After a brief introduction of RoCEv2, the second part continues from part one by explaining how parallelization strategies affect network utilization. It then discusses the Data Center Quantized Congestion Notification (DCQCN) scheme for RoCEv2, introducing key concepts such as Explicit Congestion Notification (ECN) and Priority-based Flow Control (PFC). In addition to ECN and PFC, this section covers other congestion-avoidance methods, such as packet spraying and deep buffers. The second part also delves into AI data center design choices focusing on the East-West backend network. It introduces Rail, Top-of-Rack (ToR), and Rail-Optimized designs.

Figure 1: Book Introduction.