| Technical Name | AI深度壓縮工具鏈及混合定點數CNN運算加速器 | ||
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| Project Operator | National Yang Ming Chiao Tung University | ||
| Project Host | 郭峻因 | ||
| Summary | Assisted by in-house AI deep compression toolchain (ezLabel, ezModel, ezQUANT, ezHybrid-M), the proposed technology supports automatic AI model designoptimization with the integrated performance of 120x model size reduction70x power reduction in 2D CNN model,develops a world-first 1/2/4/8-bit CNN model realized by the developed high efficiency Hybrid fixed point CNN NPU (Hybrid-NPU), which has been verified in Xilinx ZCU102 FPGAachieves the performance up to 2.5 TOPS(8-b)/ 20TOPS(1-b)@28nm technology running at 550MHz4TOPS/W energy efficiency. |
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| Scientific Breakthrough | The proposed technology attracted Wistron to launch a four-year investment with annual amount of 10M NTD to setup Wistron-NCTU embedded artificial intelligence research center in NCTU. At the same time, the proposed technology developed in our AI project also results in a total amount of 73M NTD investment from local industry. A new start-up is under cultivation to attract the Angel round investment up to 100M-180M NTDlaunch by the end of 2021. |
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| Industrial Applicability | The proposed technology attracted Wistron to launch a four-year investment with annual amount of 10M NTD to setup Wistron-NCTU embedded artificial intelligence research center in NCTU. At the same time, the proposed technology developed in our AI project also results in a total amount of 73M NTD investment from local industry. A new start-up is under cultivation to attract the Angel round investment up to 100M-180M NTDlaunch by the end of 2021. |
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| Matching Needs | 天使投資人、策略合作夥伴 |
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| Keyword | AI deep compression toolchain fast labeling automatic labeling automatic model pruning automatic model quantization fixed point AI model training high efficiency DLA design low-power NPU AI chip AI SoC | ||
| Notes | |||
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