Evaluating the Resilience of the Approximate Parallel Prefix Adder (AxPPA) Against Hardware Trojan Horse Injection

Approximate computing (AxC) offers opportunities to enhance computing efficiency by exploiting inherent error resilience. This study examines the security implications of AxC, focusing on adders, where Hardware Trojan Horses (HTH) present significant risks. We propose a framework to inject HTH into approximate adders (AxAs), specifically the Approximate Parallel Prefix Adder (AxPPA), and assess their impact. The framework, implemented in MATLAB (code available at github.com/MorganaMacedo/AxPPA_HTH), evaluates HTH injection into AxAs, analyzing error metrics (SSIM, NCC, MSE, MAE, and error-probability) and conducting logic-synthesis (LS) analysis to detect physical anomalies indicative of HTH presence. Our results demonstrate AxPPA’s resilience against HTH injection, with detection thresholds varying with approximation bit size. LS analysis shows AxPPA effectively detects HTHs across all bit approximations, outperforming LOA, particularly at K=16. AxPPA also achieves superior energy savings of 31.95% compared to LOA and notable area savings, highlighting its efficacy in HTH detection and performance optimization.