The rapid growth of edge computing applications, such as autonomous vehicles and IoT systems, necessitates real-time, low-latency inference of deep neural networks in environments with limited computational resources. Most existing distributed inference methods face significant communication overheads when handling large, complex models, especially in resource-constrained edge networks. This research explores an adaptive sparsification technique by introducing a novel peer- 2-peer (P2P) model distributed inference (MDI) framework to address these communication challenges. Adjusting the sparsity of activations based on network and device conditions can be adopted by any peer-2-peer MDI framework to improve communication overhead. By dynamically adjusting data loads, adaptive sparsification enhances performance, reduces latency, and preserves model accuracy.
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