Srinivas Rahul Sapireddy
Research interests: hardware-aware AI, RF signal intelligence, edge computing, and VLSI systems
About
I am an Assistant Professor in Electrical Engineering at Illinois State University. My research focuses on hardware-aware artificial intelligence, RF signal processing, edge-intelligent systems, and VLSI design. I develop efficient machine learning and signal-processing methods for resource-constrained computing platforms.
Hardware-Aware AI · RF · VLSIResearch Areas
- Hardware-aware machine learning for low-power edge intelligence
- RF signal processing and modulation classification using statistical and cyclostationary features
- Custom piecewise-linear activation functions for efficient neural network inference
- VLSI physical design, RTL-to-GDSII flow, and hardware acceleration
- Embedded AI deployment for communication and sensing applications
Recent Achievement
Received the Best Paper Award at RFCoN 2025 for “Re-Visiting R: Statistical Envelope Analysis” in Track 2, Session II.
IEEE RFCoN 2025📰 Latest News
Assistant Professor at Illinois State University
Joined the College of Engineering at Illinois State University as an Assistant Professor in Electrical Engineering.
Best Paper Award at RFCoN
Received Best Paper Award for statistical envelope analysis work in RF modulation classification.
Low-Power RF and Edge-AI Systems
Current work integrates RF signal processing, hardware-aware machine learning, and efficient edge deployment.
📂 Quick Access
Research group, projects, students, and lab activities
Journal articles, conference papers, and manuscripts
Courses, instructional materials, and mentoring activities
Academic background, research, teaching, and service
Technical posts on ML, RF systems, and VLSI workflows
University service, outreach, and professional activities
🚀 Research and Innovation Themes
RF Signal Intelligence
Lightweight RF signal classification using envelope statistics, feature engineering, and signal-domain analysis.
Hardware-Aware AI
Efficient neural models, custom activations, and deployment-aware learning for constrained platforms.
Low-Power Edge Systems
Runtime, memory, power, and latency-aware model evaluation for edge and embedded systems.
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🖼️ Highlights
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You can connect with me through LinkedIn.
📬 Connect with Me
I welcome research collaboration, student mentoring discussions, outreach partnerships, and academic engagement related to hardware-aware AI, RF systems, edge computing, and VLSI design.