This research presents a genetic variant discovery SoC for analyzing Next-generation sequencing data which is embedded with the processorconnected with some peripherals. With hardware-optimized algorithm sBWT, we greatly speed up the process of short reads mapping by k-ordered FM-indexsuffix grouping. After short reads mapping, we utilized de bruijn graph for sequence assembly to rebuild haplotypes,built haplotypes are compared to the reference genome to detect genome variations. The detected variations are written to files for further clinical diagnosisresearch analysis.

The objective of this project is to develop a portablewireless urine detection systemplatform for prevention of cardiovascular disease. The main idea is to develop a system-on-chip, a microelectrodemicrochannel chip to detect biomarkers concentrations in urine. And then, it will be wirelessly transmitted to a smart application platform to evaluate user’s cardiovascular status.

This project is to implement a 37-40 GHz mmWave transceiver supporting 4096 QAM modulationis utilized for long-distance 4K/8K real-time image data transmission. The 37-40GHz band is divided into 500 channels,the AI technology is used for the TRx to carry out digital calibration. The achievement is TRx module (SoC/SiP)would be installed on the UAV for wildlife resources monitoring.

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.