Jumana Mundichipparakkal
Principal Systems Architect (Performance SoC Telemetry) at Arm
Cambridge, England, United States
Summary
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Senior🎓
Top SchoolJumana Mundichipparakkal is a Principal Systems Architect specializing in SoC and platform telemetry with nine years of experience designing performance analysis methodologies for Arm IP-based platforms. She leads the architecture and delivery of Arm’s telemetry solution, including the top-down CPU performance methodology for A-class processors, and manages cross-functional teams of hardware, software and tooling engineers. Her background spans device-level workload characterization, cloud infrastructure performance for Big Data and ML, and early-career work building high-speed DSP simulators at Intel, giving her a rare full-stack view from silicon counters to system software. Jumana combines technical leadership with program-building experience—she co-launched a Women@Arm mentorship program—demonstrating a commitment to both technical excellence and talent development. Based in Cambridge, she contributes to device performance tooling publicly via GitHub and often blends modeling, benchmarking and hands-on optimization to uncover non-obvious system bottlenecks.
9 years of coding experience
6 years of employment as a software developer
Master’s Degree (Honors) Embedded Systems, Master’s Degree (Honors) Embedded Systems at Eindhoven University of TechnologyNanodegree Self-Driving Car Engineer, Nanodegree Self-Driving Car Engineer at UdacityBachelor’s Degree Electrical Electronics and Communications Engineering, Bachelor’s Degree Electrical Electronics and Communications Engineering at Visvesvaraya National Institute of Technology
English
Github Skills (11)
measurement10
ansible-collections10
workload9
arm8
mavlink5
automation4
nvidia4
interaction4
docker3
gpu2
instrumentation2
Programming languages (2)
Github contributions (5)
jumanamp/CarND-Advanced-Lane-Lines
Dec 2017 - Feb 2018
Contributions:23 pushes in 1 month
jumanamp/CarND-Vehicle-Detection
Jan 2018 - Feb 2018
Contributions:15 pushes in 20 days
vehicle-detectiondeep-learningobject-detectioncomputer-visionmachine-learning
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