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Journal Article
NEST-C: A deep learning compiler framework for heterogeneous computing systems with artificial intelligence accelerators
- Authors
- Jeman Park, Misun Yu, Jinse Kwon, Junmo Park, Jemin Lee, Yongin Kwon
- Issue Date
- 2024-10
- Citation
- ETRI Journal, v.46, no.5, pp.851-864
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원
- Language
- English
- Type
- Journal Article
- Abstract
- Deep learning (DL) has significantly advanced artificial intelligence (AI); however, frameworks such as PyTorch, ONNX, and TensorFlow are optimized for general-purpose GPUs, leading to inefficiencies on specialized accelerators such as neural processing units (NPUs) and processing-in-memory (PIM) devices. These accelerators are designed to optimize both throughput and energy efficiency but they require more tailored optimizations. To address these limitations, we propose the NEST compiler (NEST-C), a novel DL framework that improves the deployment and performance of models across various AI accelerators. NEST-C leverages profiling-based quantization, dynamic graph partitioning, and multi-level intermediate representation (IR) integration for efficient execution on diverse hardware platforms. Our results show that NEST-C significantly enhances computational efficiency and adaptability across various AI accelerators, achieving higher throughput, lower latency, improved resource utilization, and greater model portability. These benefits contribute to more efficient DL model deployment in modern AI applications.
- KSP Keywords
- AI Applications, Computational Efficiency, DL model, Dynamic Graph, Energy efficiency, Graph partitioning, Heterogeneous computing systems, Model deployment, Model portability, Multi-level, Neural processing
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
