Summary
Pradeep Ramani is a Senior Deep Learning Architect based in California with nine years of focused experience accelerating GPGPU compute for machine learning and graphics. He has driven foundational work at NVIDIA (contributing to CUTLASS and libraries used by cuDNN/cuBLAS/TensorRT) and previously shaped Intel GPU microarchitecture and feature sets across multiple generations via simulators, micro-benchmarks and hardware/software co-design. His expertise spans CUDA, OpenCL, signal processing, and hardware verification, enabling order-of-magnitude performance and power improvements through cross-stack optimization. Equally comfortable in low-level RTL/testbench development and high-performance linear algebra kernels, he blends academic rigor (MS, UC Santa Barbara, 4.0 GPA) with production shipping at scale. A lesser-known strength is his track record of building the very performance test suites and micro-benchmarks that informed silicon decisions, giving him rare insight into both silicon-level tradeoffs and ML library performance.
9 years of coding experience
7 years of employment as a software developer
Anna University, Chennai
M.S., Computer Engineering, Signal and Image Processing., CGPA : 4.0, M.S., Computer Engineering, Signal and Image Processing., CGPA : 4.0 at University of California, Santa Barbara