Multifaceted, empirical evaluation of algorithmic ideas is one of the central pillars of Information Retrieval (IR) research. The IR community has a rich history of studying the effectiveness of indexes, retrieval algorithms, and complex machine learning rankers and, at the same time, quantifying their computational costs, from creation and training to application and inference. As the community moves towards even more complex deep learning models, questions on efficiency have once again become relevant with renewed urgency. Indeed, efficiency is no longer limited to time and space; instead it has found new, challenging dimensions that stretch to resource-, sample- and energy-efficiency with ramifications for researchers, users, and the environment alike. Examining algorithms and models through the lens of holistic efficiency requires the establishment of standards and principles, from defining relevant concepts, to designing metrics, to creating guidelines for making sense of the significance of new findings. The second iteration of the ReNeuIR workshop aims to bring the community together to debate these questions, with the express purpose of moving towards a common benchmarking framework for efficiency.