Designing a compute library(such as blas, dnn, etc.) composed of various neural network operations, which are being accelerated on the rebellions' proprietary instruction set architecture(ISA)
From a functionality perspective, enhancing the functional coverage of each operation by considering operation-specific constraints(e.g., tensor shape variation, precision loss handling, etc.)
From a performance perspective, enhancing the utilization of the computational units in heterogeneous compute resources by considering operation-specific characteristics
Key Qualifications
Master's or higher degree in Electrical Engineering, Computer Science, or a related field
Thorough knowledge of neural network operations, not only for the high-level concepts but also for the low-level computation flow
Thorough knowledge of deep learning models for various applications, including vision, language, speech, etc.
Experience in model/layer-level customization in terms of computation efficiency(e.g., sparsity, reduced precision, layer decomposition, etc.)
Experience in architecture-specific parallel programming to accelerate target operations(e.g., SSE/AVX in x86, NEON in AArch, CUDA/OpenCL in GPU, etc.)
A major in computer architecture field is preferred
채용 및 업무 수행과 관련하여 요구되는 법령 상 자격이 갖추어지지 않은 경우 채용이 제한될 수 있습니다.
보훈 대상자 및 장애인 여부는 채용 과정에서 어떠한 불이익도 미치지 않습니다.
담당 업무 범위는 후보자의 전반적인 경력과 경험 등 제반사정을 고려하여 변경될 수 있습니다. 이러한 변경이 필요할 경우, 최종 합격 통지 전 적절한 시기에 후보자와 커뮤니케이션 될 예정입니다.
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NPU Library Software Engineer
Responsibilities and Opportunities
Designing a compute library(such as blas, dnn, etc.) composed of various neural network operations, which are being accelerated on the rebellions' proprietary instruction set architecture(ISA)
From a functionality perspective, enhancing the functional coverage of each operation by considering operation-specific constraints(e.g., tensor shape variation, precision loss handling, etc.)
From a performance perspective, enhancing the utilization of the computational units in heterogeneous compute resources by considering operation-specific characteristics
Key Qualifications
Master's or higher degree in Electrical Engineering, Computer Science, or a related field
Thorough knowledge of neural network operations, not only for the high-level concepts but also for the low-level computation flow
Thorough knowledge of deep learning models for various applications, including vision, language, speech, etc.
Experience in model/layer-level customization in terms of computation efficiency(e.g., sparsity, reduced precision, layer decomposition, etc.)
Experience in architecture-specific parallel programming to accelerate target operations(e.g., SSE/AVX in x86, NEON in AArch, CUDA/OpenCL in GPU, etc.)
A major in computer architecture field is preferred