Rebellions | 리벨리온대한민국 경기도 성남시 분당구 정자일로 239, 102동 8층
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
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