Technical Name AI深度壓縮工具鏈及混合定點數CNN運算加速器
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.
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.
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.
Matching Needs
天使投資人、策略合作夥伴
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
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