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Junhao Ma

Publications and source records attributed to Junhao Ma.

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OpenOpt: An Open-Source SRAM Optimizer Based on Equivalent Circuit Model

This paper proposes a co-optimization framework that jointly optimizes SRAM architecture and transistor sizing using equivalent circuit models. The framework simplifies inactive SRAM cells into equivalent RC loads and static power models, achieving up to 61.4$\times$ simulation speedup while maintaining high fidelity (read/write delay error $<$0.22%, power error $<$1.68%). A joint search space encompassing architecture parameters and device sizing integrates seven algorithms including SA, PSO, Bayesian Optimization variants, and multi-objective evolutionary algorithms. Based on FreePDK45, ablation experiments confirm complementary gains from architecture selection and transistor sizing. Among all algorithms, MOEA/D achieves the best Figure of Merit (8.2721), yielding 6.2% improvement in SNM, 73.6% reduction in area, and 42.3% reduction in peak power. The framework is publicly available at https://github.com/W1Y1K1/OpenOpt.

cs.NE

A brain-inspired paradigm for scalable quantum vision

One of the fundamental tasks in machine learning is image classification, which serves as a key benchmark for validating algorithm performance and practical potential. However, effectively processing high-dimensional, detail-rich images, a capability that is inherent in biological vision, remains a persistent challenge. Inspired by the human brain's efficient ``Forest Before Trees'' cognition, we propose a novel Guiding Paradigm for image recognition, leveraging classical neural networks to analyze global low-frequency information and guide targeted quantum circuit towards critical high-frequency image regions. We present the Brain-Inspired Quantum Classifier (BIQC), implementing this paradigm via a complementarity architecture where a quantum pathway analyzes the localized intricate details identified by the classical pathway. Numerical simulations on diverse datasets, including high-resolution images, show the BIQC's superior accuracy and scalability compared to existing methods. This highlights the promise of brain-inspired, hybrid quantum-classical approach for developing next-generation visual systems.

quant-ph

OpenYield: An Open-Source SRAM Yield Analysis and Optimization Benchmark Suite

Static Random-Access Memory (SRAM) yield analysis is essential for semiconductor innovation, yet research progress faces a critical challenge: the large gap between simplified academic models and the complexities observed in practice. The lack of open, higher-fidelity benchmarks has hindered reproducibility and transferability, as promising academic techniques often fail to carry over to more realistic settings. We present OpenYield, an open-source ecosystem that aims to narrow this gap through three contributions: (i) An SRAM circuit generator that explicitly incorporates second-order effects (interconnect/line parasitics, inter-cell leakage coupling, and peripheral-circuit variations) that are commonly omitted in academic studies. (ii) A standardized evaluation platform with a simple interface and baseline yield-analysis implementations to enable fair comparisons and reproducible research on these higher-fidelity circuits. (iii) An optimization platform for transistor-level sizing under these models, supporting reproducible studies of robustness/efficiency trade-offs. OpenYield aims to foster more reproducible and transferable progress in SRAM-yield research. The framework is publicly available at https://github.com/ShenShan123/OpenYield

cs.AR