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Qiming Guo

Publications and source records attributed to Qiming Guo.

3 recordsLinked to original sources

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

Spatial Entropy based Partitioning for Spatiotemporal Graph Unlearning

Spatiotemporal graphs underpin applications such as traffic forecasting, weather forecasting, and healthcare monitoring. Privacy regulations such as the GDPR and the CCPA require the complete removal of unauthorized data from trained models, but achieving this on a spatiotemporal graph is difficult: because information propagates globally through both spatial and temporal message passing, fully erasing a node's influence forces costly full-graph retraining. ST-graph unlearning requires both exactness and efficiency. We propose IsleNet, which uses spatial-entropy-guided partitioning to create balanced, locally coherent subgraphs and reconnects them with lightweight virtual edges. Upon an unlearning request, only the affected subgraph encoder and virtual-edge layer are retrained, ensuring exact removal with low cost. Experiments on four real-world benchmarks show that IsleNet attains up to 94% of full-graph accuracy while reducing unlearning time by up to an order of magnitude. Our code is publicly available at https://github.com/wenlu-lab/STGraphUnlearning.

cs.LG

Unlearning on Spatio-Temporal Graphs through Subgraph Virtual Edge Reconstruction

Spatio-temporal graphs are widely used in modeling complex dynamic processes such as temporal forecasting, molecular dynamics, and healthcare monitoring. Recently, stringent privacy regulations such as GDPR and CCPA have introduced significant new challenges for existing spatio-temporal graph models, requiring complete unlearning of unauthorized data. Since each node in a spatio-temporal graph diffuses information globally across both spatial and temporal dimensions, existing unlearning methods primarily designed for static graphs and localized data removal cannot efficiently erase a single node without incurring costs nearly equivalent to full model retraining. To address this, we propose CallosumNet, a spatio-temporal graph unlearning framework biologically inspired by the corpus callosum structure. CallosumNet makes two key technical contributions: (1) it reconstructs subgraphs using biologically-inspired virtual edges; and (2) it restores interlinked spatio-temporal dependencies among subgraphs via a lightweight meta-graph integration layer. Empirical results on four diverse real-world datasets show that CallosumNet achieves complete unlearning while maintaining accuracy very close to the gold model. The code is publicly available at https://github.com/wenlu-lab/STGraphUnlearning.

cs.LG