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DTSTAMP:20260730T152642Z
LOCATION:DAC Pavilion\, Exhibit Floor
DTSTART;TZID=America/Los_Angeles:20260729T150000
DTEND;TZID=America/Los_Angeles:20260729T160000
UID:dac_DAC 2026_sess296_ENGPRES424@linklings.com
SUMMARY:Using ML-Assisted Side-Channel Analysis in Simulation: A Case Stud
 y on AES-GCM
DESCRIPTION:Emrah Karagoz, Karthik Gedela, Ferhat Yaman, Amitabh Das, and 
 Sourabh Goyal (Advanced Micro Devices (AMD))\n\nThis work presents a machi
 ne‑learning‑assisted approach to pre‑silicon side‑channel analysis (SCA) f
 or AES‑GCM cryptographic IPs. Traditional SCA methods rely on explicit lea
 kage models and manual point‑of‑interest selection, which can limit accura
 cy and scalability in early‑stage hardware security verification. In contr
 ast, this study utilizes RTL‑generated power traces and profiling attacks 
 powered by convolutional and multilayer perceptron models to automatically
  learn secret‑dependent leakage without prior assumptions. The proposed fl
 ow integrates RTL simulation, power‑trace preprocessing, and ML‑based key‑
 byte classification to evaluate leakage resilience before tape‑out. Experi
 mental results using 10,000 simulated traces demonstrate successful key‑re
 covery performance, with a significant portion of AES‑GCM key bytes identi
 fied at low ranking positions across both ML models. These findings show t
 hat ML‑assisted SCA strengthens shift‑left security validation by revealin
 g vulnerabilities early in the design cycle and reducing dependence on tra
 ditional leakage modeling techniques.\n\nTopics: AI, Chiplet, Design, EDA,
  Quantum, Security, Systems\n\n
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