Shanghai Jiao Tong University · Shanghai AI Lab
Intelligent computing from algorithms to silicon
The Intelligent Computing Research Group builds efficient, secure, and scalable computing systems across brain-inspired algorithms, AI infrastructure, computer architecture, circuits, and emerging devices.
- ISCA 2026
- Neuromorphic chips and near-memory processing
- MICRO · CVPR · DAC
- Recent CCF-A results across architecture, AI, and EDA
- DATE Best Paper
- Award winner with DAC and IWLS nominations
Inside the Lab
Research that moves between theory, systems, and hardware
ICRG connects algorithm design, AI infrastructure, architecture exploration, circuit implementation, and emerging-device research in one collaborative pipeline.
Research Mission
Building the computing foundation for AI that can do reliable work
The Intelligent Computing Research Group (ICRG), led by Prof. Zhezhi He at Shanghai Jiao Tong University, studies the computing infrastructure needed for AI to move from generating content to understanding, deciding, and acting in the real world. To make such systems efficient, reliable, and deployable, we co-design neuromorphic and spiking AI, processing-in/near-memory architectures, spatial AI accelerators, AI-assisted chip design tools, and emerging-device prototypes.
Research Programs
Core research directions
Neuromorphic Intelligence
Spiking neural networks, brain-inspired learning theory, visual perception, and efficient intelligent systems.
02Processing-in-Memory Systems
Near-memory acceleration, SRAM/ReRAM-based PIM, data-centric architecture, and compiler support.
03AI for EDA and Chip Design
Machine-learning-assisted EDA, LLM-based Verilog generation, logic synthesis, and design automation.
04Efficient and Secure AI
Model compression, dynamic inference, adversarial robustness, privacy, and trustworthy deployment.
05Emerging Devices and Silicon
Post-CMOS devices, device-aware computing, neuromorphic circuits, and chip prototype validation.
Recent Momentum
Recent results in top research venues
7 publications in MICRO, ISCA, CVPR, and HPCA.
Recent work appeared in MICRO, ISCA, CVPR, HPCA, ICCAD, DAC, TCAD, and ASPLOS.
Recent recognition includes Best Paper Nomination at DAC 2025, Best Student Paper Award Nomination at IWLS 2025, and Best Paper Nomination at ICCD 2023.
Join ICRG
Prospective students
Before contacting us, please review our research directions and recent publications to understand the problems we study and the approaches we take. Consider how your research interests and background align with our work.