Energy-Efficient Computing and AI Chip

Overview

We mainly conduct research on integrated circuits and systems as well as EDA for IC and FPGA, aiming to push the limits of performance and energy-efficiency of electronic systems. More specifically, we are working on integrated circuits and systems for artificial intelligence and computer vision, hardware security and energy-efficient power supply for multi-core.

Representative Work

2020 DAC - DVFS-Based Scrubbing Scheduling for Reliability Maximization on Parallel Tasks in SRAM-based FPGAs

2020 IEEE TCAS-II - Histogram of Oriented Gradients Feature Extraction from Raw Bayer Pattern Images

2020 IEEE TCAS-I - Cascaded Form Sparse FIR Filter Design

2019 IEEE TCAD - A system-level framework for online power and supply noise prediction

2019 IEEE TCAS-I - Design of Sparse FIR Filters with Reduced Effective Length

2018 DAC - A system level framework for on-line supply noise prediction

2016 IEEE TCAD - Machine learning for noise sensor placement and full-chip voltage emergency detection

2015 DAC - A Statistical Methodology for Noise Sensor Placement and Full-Chip Voltage Map Generation
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7167278