Nicola Lancellotti is a compiler engineer at Arm with a decade of experience building and optimizing compiler and ML inference stacks. He has contributed to the high-profile Apache TVM project, adding TFLite quantized operators and enabling support for Arm Ethos-U NPUs with pooling, binary elementwise ops, and a rolling_buffer scheduling primitive. Academically rigorous—he holds both bachelor’s and master’s degrees in Computer Science with top honors from Sapienza and Università degli Studi di Salerno—he combines deep theory with practical, production-focused compiler work. Based in the UK, Nicola brings a pragmatic focus on efficient, hardware-aware code generation for embedded and accelerator targets, and a track record of improving real-world ML deployment on Arm platforms.
10 years of coding experience
Master's degree, Computer Science, 110/110 Cum Laude and Honourable Mention, Master's degree, Computer Science, 110/110 Cum Laude and Honourable Mention at Università degli Studi di Salerno
Bachelor's degree, Computer Science, 110/110 Summa Cum Laude, Bachelor's degree, Computer Science, 110/110 Summa Cum Laude at Sapienza Università di Roma
Open deep learning compiler stack for cpu, gpu and specialized accelerators
Role in this project:
ML Engineer
Contributions:173 reviews, 27 commits, 29 PRs in 1 year 11 months
Contributions summary:Nicola contributed to the TVM compiler stack by implementing features for the TFLite frontend, adding support for quantized RESIZE_BILINEAR and TANH operators. The user also worked on supporting Arm(R) Ethos(TM)-U NPU by adding support for the max and average pooling operators. Further contributions include supporting binary elementwise operators and a rolling_buffer scheduling primitive for the Arm(R) Ethos(TM)-U NPU.
Contributions:11 pushes, 2 branches, 5 tags in 5 years 8 months
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