Jiahao Song: h-index, Total Citations, and Citation Map
Jiahao Song's h-index is 15 (23 i10-index, 589+ total citations across 42+ publications) according to Google Scholar as of May 2026. Jiahao Song is affiliated with Postdoctoral Scholar, UC San Diego.
Jiahao Song is a researcher affiliated with Postdoctoral Scholar, UC San Diego, specializing in memory design, compute-in-memory, analog computing. Their work has been cited 589 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Jiahao Song's Citation Metrics
Bibliometric impact based on 42 indexed publications.
- H-Index
- 15
- i10-Index
- 23
- Total Citations
- 589
- Citing Countries
- 32
As of May 2026.
Jiahao Song has an h-index of 15 and 589 total citations across 42 publications, with research cited by institutions in 32 countries.
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Global Impact Map
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Top Cited Works
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TD-SRAM: Time-domain-based in-memory computing macro for binary neural networks
202182
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
160 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher pioneered time-domain SRAM-based in-memory computing macros for binary neural networks, establishing a foundational architecture for efficient, sparse, and signed-operand AI hardware acceleration.
The researcher developed a scalable, reconfigurable charge-domain SRAM in-memory computing macro, advancing robust transpose operations and sparsity-optimized multi-mode MAC capabilities for efficient neural network acceleration.
The researcher developed a calibration-free 4-bit computing-in-memory macro using eDRAM, establishing a foundation for efficient, multi-precision edge AI inference and on-device fine-tuning systems.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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