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Senling Wang

Publications and source records attributed to Senling Wang.

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LOCO: A Low-Cost SNU-Self-Resilient Latch Using an Output-Split C-Element

As the CMOS technology enters nanometer scales, integrated circuits (ICs) become increasingly sensitive to radiation-induced soft errors, which can corrupt the state of storage elements and cause severe reliability issues. Many hardened designs have been proposed to mitigate soft errors by using filtering elements. However, existing filtering elements only protect their inputs against soft errors and leave their outputs unprotected. Therefore, additional filtering elements must be added to protect outputs, resulting in extra overhead. In this paper, we first propose a novel Output-Split C-element (OSC) to protect both its input and output nodes, and then a novel LOw-COst single-node-upset (SNU) self-resilient latch (LOCO) to use OSCs to achieve both soft error resilience and low overhead. The usage of OSCs effectively reduce the short-circuit current of the LOCO latch during switching activities. Furthermore, the usage of clock gating and high-speed path reduces power consumption and delay, respectively. Compared with state-of-the-art SNU-resilient hardened designs, the LOCO latch achieves 19% fewer transistors, 63.58% lower power, 74% less delay, and 92% lower power-delay-product (PDP) on average. In addition, the LOCO latch exhibits better stability under variations in PVT (Process, Voltage, and Temperature).

cs.GT

A Spatio-Temporal Graph Neural Networks Approach for Predicting Silent Data Corruption inducing Circuit-Level Faults

Silent Data Errors (SDEs) from time-zero defects and aging degrade safety-critical systems. Functional testing detects SDE-related faults but is expensive to simulate. We present a unified spatio-temporal graph convolutional network (ST-GCN) for fast, accurate prediction of long-cycle fault impact probabilities (FIPs) in large sequential circuits, supporting quantitative risk assessment. Gate-level netlists are modeled as spatio-temporal graphs to capture topology and signal timing; dedicated spatial and temporal encoders predict multi-cycle FIPs efficiently. On ISCAS-89 benchmarks, the method reduces simulation time by more than 10x while maintaining high accuracy (mean absolute error 0.024 for 5-cycle predictions). The framework accepts features from testability metrics or fault simulation, allowing efficiency-accuracy trade-offs. A test-point selection study shows that choosing observation points by predicted FIPs improves detection of long-cycle, hard-to-detect faults. The approach scales to SoC-level test strategy optimization and fits downstream electronic design automation flows.

cs.LG