The static nature of sparsity in ML models, as leveraged by ESPIM's techniques, can be beneficial for efficient sparse model training and adaptation on the edge. By utilizing the static sparsity information known during training, ESPIM can optimize the scheduling and data dependencies for sparse models, enabling faster inference and reduced energy consumption. To enable efficient sparse model training and adaptation on the edge, ESPIM's techniques can be extended to incorporate dynamic reconfiguration capabilities based on evolving sparsity patterns. This adaptability can allow the system to adjust its operations in real-time to accommodate changes in sparsity, ensuring optimal performance for sparse model training and adaptation scenarios.