remarks, and improved experimental results on CIFAR10-binary, finding a model with 76.83% accuracy and WM2 2KB and a model with 74.87% accuracy and WM,MS2KB, both of which outperform Bonsai

We thank the reviewers for their valuable feedback. This rebuttal includes further experiments to address the reviewers' These ablation results support the design choices made in SpArSe in the context of memory constrained MCUs. On MNIST, SpArSe achieves accuracy of 99.17% with 1.45e3 parameters, compared to 99.15% accuracy SpArSe would not work with the design choices made in previous NAS works, especially [23]. Reproducability (R1) We are happy to make the implementation publicly available upon acceptance. We argue that: 1) SpArSe addresses a significant gap in the community, i.e. model design for V alidity of claim on line 66 (R1) Our claim is true for WM 2KB, but we will revise that sentence for clarity.