A Meticulous Guide to Advances in Deep Learning Efficiency over the Years (alexzhang13.github.io)

A newly published comprehensive guide details the evolution of efficiency in deep learning over the past four decades, spanning algorithms, hardware, and software advancements. This chronological overview is intended to provide readers—especially researchers and students—with a clearer understanding of how the field has progressed and the significant breakthroughs that have shaped it. It emphasizes the importance of technological developments, such as NVIDIA's tensor cores capable of executing matrix multiplications at unprecedented speeds, and the growing capabilities of libraries and compilers that have supported these advancements. The significance of this guide lies in its ability to synthesize a vast amount of information into a more digestible format, offering a macroscopic lens on deep learning's trajectory rather than an exhaustive survey. By highlighting landmark innovations from the introduction of backpropagation to modern training methods on extensive datasets, such as Meta's Llama 3.1 model trained on 15 trillion tokens using 16,000 NVIDIA H100 GPUs, the guide illustrates a marked evolution in both computational capability and methodological efficiency. This serves as a crucial resource for the AI/ML community, underscoring the ongoing developments that continue to propel the field forward.