Pranathi Vasireddy: h-index, Total Citations, and Citation Map
Pranathi Vasireddy's h-index is 4 (2 i10-index, 42+ total citations across 11+ publications) according to Google Scholar as of May 2026. Pranathi Vasireddy is affiliated with AMD.
Pranathi Vasireddy is a researcher affiliated with AMD, specializing in VLSI Design and Verification, Hardware designing, Synthesis. Their work has been cited 42 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Pranathi Vasireddy's Citation Metrics
Bibliometric impact based on 11 indexed publications.
- H-Index
- 4
- i10-Index
- 2
- Total Citations
- 42
- Citing Countries
- 2
As of May 2026.
Pranathi Vasireddy has an h-index of 4 and 42 total citations across 11 publications, with research cited by institutions in 2 countries.
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Top Cited Works
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Efficiency, expressivity, and extensibility in a close-to-metal npu programming interface
202517
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.
Self-citation 50% — above the ~30% level that commonly triggers an RFE.
29 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher developed hardware accelerator threads for unstructured sparse data processing, extending the framework to vector extensions via near-data processing.
The researcher developed a close-to-metal NPU programming interface designed to balance efficiency, expressivity, and extensibility, establishing a foundational framework for hardware-aware neural processing unit software development.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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