The Efficient Machine Intelligence (EMI) research lab focuses on designing energy-efficient digital AI hardware (ASIC & FPGA) for edge computing, emphasizing real-time AI-based radio frequency and video/audio signal processing. 

We strongly believe that hardware-software co-design is the path to realizing the ultimate goal of intelligent edge solutions for complex real-world applications.

New master projects are available!

July 18, 2024

Dr. Chang Gao has been awarded a prestigious Veni grant of €320,000 from the Dutch Research Council (NWO) for the project "Energy-Efficient Real-Time Edge Intelligence for Wearable Healthcare Devices." This innovative research aims to develop new software and hardware technology to enhance the intelligence and efficiency of wearable healthcare devices like eye movement trackers, hearing aids, and heart rate monitors. By processing data and running AI algorithms directly on these devices using specialized hardware accelerators, the project seeks to enable instant health monitoring, improve privacy, and reduce energy consumption. This work could transform personal health monitoring, making it faster, more secure, and widely accessible while contributing to the development of smarter, more sustainable wearable devices.

May 29, 2024

Congratulations to Qinyu Chen (Assistant Prof. @ Leiden Univ.) and Chang Gao (Assistant Prof. @ TU Delft) for their paper at ISCAS 2024. The paper is titled "Epilepsy Seizure Detection and Prediction using an Approximate Spiking Convolutional Transformer." The paper won the Best Paper - Honourable Mention Award from the IEEE ISCAS Neural Systems and Application Technical Committee (NSATC).

April 17, 2024

We're thrilled to announce our collaboration with GlobalFoundries in the GF 12LP+ University Partnership Program to support our research on Low-Power AI Hardware Accelerators using the cutting-edge 12 nm technology for enabling energy-efficient signal processing in transmitters for future wireless communications technologies and potentially useful for many other applications such as augmented reality and wearable healthcare.

Feb 2, 2024

The premier International Microwave Symposium (IMS) has accepted our paper "MP-DPD: Low-Complexity Mixed-Precision Neural Networks for Energy-Efficient Digital Pre-distortion of Wideband Power Amplifiers," mainly done by our PhD students Yizhuo Wu and Ang Li. The paper was ranked in the Top 50 and invited to the IEEE Microwave and Wireless Technology Letters. This paper proposes a novel DPD with Fixed-Point-Floating-Point Mixed-Precision parameters and intermediate states, which can reduce DPD inference power consumption by 2.6X on 160MHz 1024-QAM OFDM signals without losing linearization performance (-38dB EVM).

MP-DPD is trained using our recently released OpenDPD framework, available at https://github.com/lab-emi/OpenDPD.

Jan 19, 2024

We are thrilled to release OpenDPD: An Open-Source PyTorch-based End-to-End Learning & Benchmarking Framework for Wideband Power Amplifier Modeling and Digital Pre-Distortion (DPD). Authored by Yizhuo Wu, Gagan Deep Singh, Mohammad Reza Beikmirza, Leo de Vreede, Morteza Alavi, and Chang Gao (https://arxiv.org/abs/2401.08318). DPD enhances signal quality in wideband RF power amplifiers (PAs) and is a critical module in future 6G or Wi-Fi 7 wireless communication systems. OpenDPD comes with a free digital power amplifier I/Q dataset for you to train and benchmark machine learning (ML)/artificial intelligence (AI)-based DPDs and fairly compare them with other works. We will collaborate closely with our industrial partners to update this infrastructure periodically. OpenDPDv2 will come later this year with also free analog PA datasets.

OpenDPD code, datasets, and documentation are publicly available at https://github.com/lab-emi/OpenDPD. This work will be presented as a lecture at the 2024 IEEE International Symposium on Circuits and Systems (ISCAS), Singapore, in the Special Session: RFIC & AI: Pioneering New Wireless Communications.