Deep learning compilers (DLCs) have become the standard approach for optimizing edge inference performance, employing techniques such as operator fusion, loop tiling, and scheduling to meet stringent resource constraints. Yet, the security implications of these optimizations remain largely unexplored. In this work, we investigate shared-memory side-channel attacks on edge SoCs and analyze how compiler optimizations reshape the leakage surface. Our study reveals that identical operators can exhibit distinct shared-resource access patterns under different compilation strategies, resulting in divergent attack outcomes. To address this, we introduce the Confusion Variance Score (CVS), a metric that quantifies compilation-induced security by measuring confusion in time-series resource traces (e.g., DRAM bandwidth). CVS integrates multidimensional dynamic time warping with statistical morphological features to ensure temporal robustness, and shows a strong negative correlation (Spearman r ≈ −0.9394) with practical attack error rates. Finally, we demonstrate the feasibility of CVS-guided compilation in TVM and TensorRT, achieving a 24 % increase in attack error rate compared to default strategies, while limiting inference latency overhead to under 5 %.

The generalized arc consistency (GAC) algorithm is the prevailing solution for alldifferent constraint problems. The core part of GAC for alldifferent constraints is excavating and enumerating all the strongly connected components (SCCs) of the graph model. This causes a large amount of complex data structures to maintain the node information, leading to a large overhead both in time and memory space. More critically, the complexity of the data structures further precludes the coordination of different optimization schemes for GAC. To solve this problem, the key observation of this paper is that the GAC algorithm only cares whether a node of the graph model is in an SCC or not, rather than which SCCs it belongs to. Based on this observation, we propose AllDiffbit, which employs bitwise data structures and operations to efficiently determine if a node is in an SCC. This greatly reduces the corresponding overhead, and enhances the ability to incorporate existing optimizations to work in a synergistic way. Our experiments show that AllDiffbit outperforms the state-of-the-art GAC algorithms over 60%.