Robotic prosthesis control
Recurrent neural network control with the EdgeDRNN accelerator.
With AMBER Lab, Caltech Watch on YouTubeAlgorithms ↔ Hardware
Efficiency, by design.
We study neuromorphic algorithm–hardware co-design for energy-efficient edge intelligence. Our work bridges learning methods, sensing systems and the hardware that makes them practical.
EMI on GitHub
Open-source research.
Explore the code, datasets and models behind our work.
RF & mixed-signal AI
OpenDPD
An end-to-end learning framework for power amplifier modeling and digital predistortion, with measured datasets and reproducible benchmarks.
Code · Datasets · BenchmarksSpeech denoising
CleanUMamba
A compact Mamba network for speech denoising, with channel pruning, training tools and pretrained models for efficient audio processing.
Code · Pretrained models · PruningRadar sensing
AIRHAR
The official RadMamba implementation for radar-based human activity recognition, with data preparation, model training and evaluation workflows.
Code · Training · EvaluationResearch in action
From idea to demonstration.
Spoken digit recognition
Real-time spoken digit recognition using EdgeDRNN.
Read the EdgeDRNN paper Watch on YouTubeNeuromorphic computing
Interfacing EdgeDRNN with a dynamic audio sensor.
Explore the silicon cochlea Watch on YouTubeResearch support
Shared ambition.
Our research is supported by public funding, industry collaborations and a community of academic partners.
Grants, sponsors & awards