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Xingran Huang

Publications and source records attributed to Xingran Huang.

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Clock-Gating Insertion Strategies on an Open-Source MSP430 Core: A Reproducible PPA Study and a Gate-Level Simulation Caveat

Clock gating, the standard technique for cutting dynamic power, is introduced either as hand-written behavioral clock gates at the register-transfer level (RTL) or as integrated clock-gating (ICG) cells inserted automatically during synthesis; the two are widely treated as interchangeable. In this paper we show, on a real open-source 16-bit microcontroller core (openMSP430) synthesized with a 32 nm standard-cell library, that they are not equivalent in practice: behavioral latch-based RTL gating is functionally correct in ideal RTL simulation (10/10 self-checking testcases, identical to the ungated baseline) yet fails at gate level: the gated multiplier result is never captured and reads zero, while tool-inserted ICG cells pass gate-level simulation cleanly (10/10). We root-cause the failure to a hold race introduced by the late latch+AND gated clock, and show it persists across eight simulation configurations including full Standard Delay Format (SDF) back-annotation, not a simulator-setting artifact. We then quantify the power/area/timing (PPA) impact of three gating strengths: RTL behavioral (Opt1), synthesis ICG (Opt2), and both (Opt3), against the ungated baseline, across four workloads and three process corners (ss/tt/ff). The benefit is corner-robust: ICG (Opt2) cuts dynamic power by 74-81% and total power by 25-30% at every corner. We also show that in this leakage-dominated 32 nm regime the total-power win comes from the area/leakage reduction that gating brings (leakage -24 to -30%), not from the large dynamic saving, which instead dominates active-mode energy. Our recommendation for low-power design on open-source cores is to prefer tool-inserted ICG cells over hand-written behavioral clock gates. The full flow (Design Compiler synthesis, PrimeTime PX power, and self-checking verification) is released as an open artifact.

cs.AR

Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models

We introduce Advertisement Embedding Attacks (AEA), a new class of LLM security threats that stealthily inject promotional or malicious content into model outputs and AI agents. AEA operate through two low-cost vectors: (1) hijacking third-party service-distribution platforms to prepend adversarial prompts, and (2) publishing back-doored open-source checkpoints fine-tuned with attacker data. Unlike conventional attacks that degrade accuracy, AEA subvert information integrity, causing models to return covert ads, propaganda, or hate speech while appearing normal. We detail the attack pipeline, map five stakeholder victim groups, and present an initial prompt-based self-inspection defense that mitigates these injections without additional model retraining. Our findings reveal an urgent, under-addressed gap in LLM security and call for coordinated detection, auditing, and policy responses from the AI-safety community.

cs.CR