Our projects
Analog Vision System for Robots
In this project, we utilize neuromorphic chips based on analog neural networks to process visual inputs in real-time. By mimicking the neural computations of the human retina, the system can recognize and track objects with reduced power consumption. The analog nature of the chip allows for continuous signal processing, enabling robots to adapt to dynamically changing environments seamlessly.
Spike-based Sound Localization
Harnessing the power of spiking neural networks, this project focuses on creating an auditory system for drones. The neuromorphic chip processes auditory signals in a manner similar to the human cochlea, identifying the direction and source of sounds. The spiking nature of the network ensures energy efficiency and fast response times, allowing drones to react quickly to auditory cues in their surroundings.
Analog Neural Network for Medical Diagnostics
Leveraging the analog neural capabilities of neuromorphic chips, this project aims to develop a medical diagnostic tool that can analyze complex biological signals. By emulating the neural computations of the human brain, the system can detect patterns and anomalies in ECG, EEG, and other medical data. The continuous analog processing offers enhanced sensitivity and accuracy, making early disease detection more feasible.
