Hugh Perkins is a highly respected AI researcher and software engineer recognized for his profound contributions to the deep learning community, especially in the realm of GPU acceleration and open-source frameworks. He is known for his expertise in CUDA and OpenCL, and for developing tools and libraries that enable efficient deep learning on various hardware. Hugh has been instrumental in projects aimed at making deep learning more accessible and performant, including significant work on GPU backends for frameworks like Torch and PyTorch, and tools for interoperability like Coriander (OpenCL to CUDA translation). His work demonstrates a deep understanding of both high-level AI concepts and low-level hardware optimization.
Hugh Perkins's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Developed Coriander, an open-source tool that translates OpenCL 1.2 kernels to CUDA C++, enabling easier migration of OpenCL-based applications to the NVIDIA CUDA ecosystem.
Made significant contributions to the Torch deep learning framework, particularly its CUDA (e.g., cunn, cutorch) and OpenCL (e.g., cltorch, clnn) backends, which were foundational for GPU-accelerated deep learning.
Authored some of the earliest and most comprehensive OpenCL-based deep learning libraries like DeepCL, showcasing the potential of OpenCL for neural network computations well before widespread adoption.
Worked at NVIDIA, contributing his expertise to enhance their deep learning software stack and support the broader AI community leveraging NVIDIA GPUs.
Yale University - Year 2016
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