"We will demonstrate the following three technical achievements of our joint project:
1. Deployment of HarDNet on GPU (power consumption:  200 Watts)
2. Deployment of HarDNet on FPGA (power consumption: several tens of Watts) [winning 2nd place in the FPGA track, LPCVC 2020]
3. Deployment of HarDNet on lightweight edge devices such as Raspberry Pi (power consumption: single-digit,  10 Watts) [winning 3rd place in the DSP track4th place in the CPU track, LPCVC 2020]"

Being performed on various computing platforms such as GPU, FPGAAI edge device, HarDNet can consistently achieve highly competitive performance in terms of speedaccuracy. Especially, for the application of real-time semantic segmentation, HarDNet is ranked first around the worldhas been recognized as "state of the art" (SOTA). Not only have we already deployed HarDNet on various platforms with different power budgets, but also we have been applying HarDNetits variants on a variety of computer vision tasks besides those already done, including our LPCVC＇20 winning projects.

"1. 矽谷智慧語音晶片大廠採用本團隊開發之RNN加速方案，其高階AI語音晶片已於2020下線，該晶片預估產值上看億元美金。_x000D_
2. 台灣記憶體晶片製造大廠與本團隊共同合作AI computing in memory技術，開創下一代晶片新藍海。_x000D_
3. 成立新創公司，為產業提供動能，為國家培養AI人才。"

天使投資人、策略合作夥伴