ReaLLMASIC LabAI for Chips. Chips for AI.
At Brown University, we develop AI-assisted chip design tools, energy-efficient computing hardware, and integrated circuits for sensing and emerging applications.
Latest from the Lab
- A visit to URI to share our research
- Honored to receive a 2026 DARPA Young Faculty Award!
- Welcoming AMD to the lab: conversations on AI for chip design and verification!
- SIMFONY presented at ESSERC 2026
- Exploring AI-assisted analog design at MWSCAS 2026
- Ruichen Qi presents CMOS and emerging-memory co-design at TMRC 2026
Research Areas
Exploring the Frontiers of Technology
Our research spans multiple domains within electrical and computer engineering, with a focus on practical applications and real-world impact.
Circuits for AI
Edge AI/LLM Systems Co-Design
Energy-efficient accelerators and HW/SW co-design for edge AI and LLM inference under tight power, latency, and area constraints.
AcceleratorsCo-DesignLLM Inference
AI for Circuits
AI-Powered EDA & Circuit Exploration
LLM-assisted analog layout and self-exploration methods to discover and optimize novel circuit topologies automatically.
LLMAnalog LayoutGARL
Signals for Health
Biomedical Sensing
Wearable sensors and signal processing for robust biomedical monitoring and diagnostics.
BiosignalsWearablesDSPHealth
CMOS for Life Science
Molecular Synthesis, Sensing and Sequencing
CMOS platforms for molecular synthesis, sensing, and sequencing, enabling scalable life-science assays and biointerfaces.
BioelectronicsBiosensorSequencing
Cryo-Ready
Quantum & Cryogenic Circuits
Cryogenic CMOS quantum error correction (QEC) co-processor for superconducting quantum chips.
CryogenicQubit ControlReadout
Trust by Design
Hardware Security
Root-of-trust, probing detection, and secure hardware design for trustworthy systems.
RoTProbingSecure BootPUF
Tapeouts
Our Tapeouts
