CORTEXA
← Browse
arxivcs.CRcs.AR2026-07-25

SPARC: Automated Root-Cause Analysis of Pre-Silicon Power Side-Channel Leakage in the Processor Design Flow

Andrija Nešković, Christian Ewert, Mladen Berekovic, Saleh Mulhem

Power-Side-Channel Leakage (PSCL) originates from architectural and micro-architectural artifacts in a processor and poses a severe threat to the confidentiality of cryptographic software. Consequently, pre-silicon PSCL evaluation is indispensable for secure hardware design. Existing frameworks are either limited by poor simulation scalability or fail to attribute leakage to the correct hardware signals and software instructions, thereby impeding a comprehensive root-cause analysis. This paper presents SPARC, an automated framework for pre-silicon PSCL evaluation and root-cause analysis. SPARC leverages macro-cell-level Information Flow Tracking (IFT) augmented with enhanced shadow logic that tags switching activity originating from secret-dependent data. By isolating this activity, SPARC applies statistical leakage tests to detect PSCL, while simultaneously attributing the leakage to specific hardware signals and mapping those signals to the corresponding software instructions. This approach thus delivers a full end-to-end leakage evaluation and root-cause analysis for both hardware and software. To demonstrate and validate SPARC, PSCL of multiple open-source RISC-V CPUs, encompassing 32-bit and 64-bit cores with both in-order and out-of-order pipelines, is evaluated across a range of cryptographic workloads, including masked and unmasked AES and ML-KEM (CRYSTALS-Kyber-512). SPARC recovers known leakage sources as a sanity check and identifies specific microarchitectural leakage sources, achieving an 8x per-trace simulation speedup over previously shown approaches on comparable designs. By enabling precise and scalable root-cause analysis at the pre-silicon stage, this work provides a practical framework to mitigate PSCL early in the design flow, thereby strengthening the security of future processors.

View free PDFSource page

Related papers

arxivcs.AIcs.ARcs.CR2026-07-28

ContractHIL-HLS: Contract-Aligned Multi-Agent Workflow with Hardware-in-the-Loop Feedback for HLS Design

Jingbo Zhang, Haoxiang Sun, Wenbo Wang, Wenbo Zhang

This paper presents ContractHIL-HLS, a contract-aligned multi-agent workflow for practical high-level synthesis (HLS) engineering. The workflow makes three contributions. First, it introduces a structured contract as the semantic-alignment and task-execution artifact that transla…

View free PDFSource page
arxivcs.CRcs.AIcs.AR2026-07-16

Lazy Arithmetic using Systolic Arrays for Closing the Verification Gap on Embedded Systems

Taisa Kushner, Ryan McCleeary, Martin Brain

Complex algorithms such as deep neural networks are increasingly being deployed on embedded, resource constrained platforms. However, existing hardware and software schemes for implementing these models on the edge fall short, particularly for safety-critical applications such as…

View free PDFSource page
arxivcs.ARcs.CRcs.LG2026-07-04

SABLE: An NDA-Safe Closed-Loop LLM Framework for Analog Circuit Optimization in Industrial EDA Flows

Xunqi Li, Chris H. Kim

Large language models (LLMs) can propose circuit-optimization decisions, but industrial analog flows cannot expose foundry PDK content, proprietary schematics, absolute simulation paths, or license-bound tool state to a cloud endpoint. We present SABLE (Safe Analog Boundary for L…

View free PDFSource page
arxivcs.ARcs.CR2026-07-30

Demystifying DRAM Read Disturbance: Bridging the Gap Between Experimental Characterization and Device-Level Modeling of RowHammer and RowPress Phenomena

Haocong Luo, Longda Zhou, Ataberk Olgun, İsmail Emir Yüksel, Nisa Bostanci, Zhigang Ji, et al.

DRAM read disturbance, like RowHammer and RowPress, is a critical robustness issue where accessing DRAM can cause unintended bitflips in other unaccessed DRAM locations. DRAM read disturbance bitflips significantly impact the safe, secure, and reliable operation of DRAM-based com…

View free PDFSource page
arxivcs.CRcs.AIcs.ARcs.DCcs.LG2026-07-20

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption

Sahaj Majavdia, Mahdi Taheri

Structured pruning is essential for making neural network inference feasible under homomorphic encryption (HE), yet its impact on model reliability has remained unexplored. This paper presents a systematic reliability characterization of pruned CKKS-encrypted neural networks and…

View free PDFSource page
arxivcs.LGcs.ARcs.CR2026-07-26

ADVERSARIAL: And-Inverter Graph-Assisted Hardware Trojan Detection At Scale

Yaroslav Popryho, Debjit Pal, Inna Partin-Vaisband

Modern System-on-Chip (SoCs) often contain hundreds of millions to tens of billions of gates, making existing Hardware Trojan (HT) detection methods impractical due to their immense scale. The proposed approach incorporates symbolically enabled learning by modeling flattened gate…

View free PDFSource page