Ciarán O' Rourke

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Developed an internal cross-platform ML inference library; • Supports constructing arbitrary ML pipelines from JSON configurations. • Leverages modern neural network hardware on edge-devices. • Supports intermingling CPU, GPU, and NPU workloads. • Generates optimal asynchronicity for complex execution graphs.

Deep Render are designing a deep learning video compression pipeline. My role was mostly split between development of Deep Render's custom implementation of the rANS (Range Asymmetric Numeral Systems) entropy coder written in C++ and converting our research ML pipeline into a viable product on consumer devices. • Performance analysis and optimisation of our entropy coder, pertaining to runtime, energy usage and bitstream size. • Keeping our entropy coder up-to-date with our overall pipeline requirements. • Facilitating feature additions that require information to pass from our encoding pipeline to our decoding pipeline through injection of metadata into the bitstream. • Interfacing with researchers on model design decisions, particularly regarding the potential runtime performance impact of proposed additions/changes. • Porting of research pipeline for consumer device architectures (eg. Snapdragon/Apple chipsets), making use of NPUs and GPUs

• Performance analysis and optimisation for external researchers as part of EuroHPC, generally scientific simulations in C++ for distributed architechtures • Development of a C++ webserver (Deimos) for heirarchical storage management backend • Conference presentation on development of Deimos. • Design of API for heirarchical storage management solving data management for exascale architectures • Development and maintainance of GoLang RestAPI handling worklow management for large-scale execution (Lexis) • Integration of workflow-related features into the Lexis front-end; including visualisation of arbitrary workflow topologies • Preparation of material and delivery of a number of HPC-related courses