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Manas Kulkarni@manaskulkarni02
1
Events attended
1
Submissions made
3+ years of experience
About me
Driven by the challenge of turning complex hardware problems into reliable, high-impact engineering solutions, my journey has taken me from leading embedded systems development for a Formula Student electric race car, to designing and validating high-reliability circuits for nuclear data acquisition systems, to working on GPU physical design at Intel. Across each step, I’ve been drawn to roles at the intersection of deep technical problem-solving, system-level thinking, and real-world execution. At Orion Racing India, I worked on the embedded systems backbone of an electric race car, including ECU hardware development, signal conditioning, CAN communication, telemetry, and data acquisition. At SLB, I contributed to hardware designed for harsh environments, including 175°C-rated circuit applications, where reliability and validation were critical. These experiences built a strong foundation in hardware design, testing, and system integration. That foundation led me deeper into chip design and silicon implementation. During my MSE in Electrical Engineering at the University of Pennsylvania, I worked on projects spanning digital VLSI, analog circuits, computer architecture, and low-power design, including the RTL-to-GDSII implementation of a low-power RISC-V processor. Today at Intel, I work on GPU physical design signoff, design methodology, debug, and automation, helping improve flow robustness and solving timing and power integrity challenges in advanced-node silicon. I’m particularly interested in physical design, signoff, low-power VLSI, circuit design, and AI in EDA, especially where smarter tools and automation can improve how chips and systems are designed, analyzed, and debugged. Feel free to connect if you’d like to discuss any of these areas.
🤝 Top Collaborators
🤓 Latest Submissions
Spice_Wizard
Spice Wizard is a simulator-in-the-loop assistant for analog circuit design. It retrieves a known-working SPICE template, uses an open-weight LLM on AMD MI300X with PyTorch and ROCm to adjust permitted component values, and verifies every candidate in real LTspice. Waveforms are parsed to measure gain, bandwidth, phase, and settling against the requested tolerance; failures feed a constrained retry loop. For users without suitable template netlists, Spice Wizard includes a fine-tuned fallback model, also fine-tuned on AMD hardware, to generate a starting circuit. The agent accepts natural-language chat queries in a smooth desktop GUI and returns a netlist plus numeric verification report. Our cloud API path is currently blocked by the event firewall, so the demo shows manual MI300X inference and the working GUI flow locally. Passing results are logged as verified training pairs, supporting a self-improving dataset.
12 Jul 2026
