The field of machine learning and neuromorphic computing is moving towards energy efficiency, with a focus on developing optimized libraries, frameworks, and architectures that reduce power consumption without sacrificing performance. Researchers are exploring novel approaches such as spiking neural networks, distributed neural networks, and mixed-precision techniques to achieve ultra-low power consumption. These advancements have the potential to enable the deployment of machine learning models on resource-constrained devices, such as wearable nodes and edge devices, and to improve the overall efficiency of computationally restricted systems. Noteworthy papers in this area include EmbeddedML, which introduces a training-time-optimized and mathematically enhanced machine learning library, and Spiking Vocos, which proposes a novel spiking neural vocoder with ultra-low energy consumption. Additionally, papers like NEURAL and MaRVIn present innovative architectures and frameworks that support energy-efficient execution of spiking neural networks and mixed-precision deep neural networks, respectively.