SynSense is the world’s leading supplier of neuromorphic intelligence and application solutions.
At SynSense, our vast expertise in designing low-power neuromorphic circuits, and our knowledge in theory of neuromorphic, cortical computation and machine learning, gives us a unique perspective and vision beyond what is believed to be possible with today’s technologies.
Our full-custom neuromorphic processors will be used in a variety of artificial intelligence edge computing applications that require ultra-low-power and ultra-low-latency features, including autonomous robots, always-on co-processors for mobile and embedded devices, wearable healthcare systems, security, IoT applications, and computing at the network edge. As a “full-stack” neuromorphic engineering company, SynSense delivers complete solutions, including custom hardware and software configurations to meet specific application needs.
Smart Vision Processing
SynSense is developing dedicated event-driven neuromorphic processors for real-time vision processing. The ultra-low-power and ultra-low-latency of our processors pave the path for always-on IoT devices and edge-computing applications like gesture recognition, face or object detection, location, tracking and surveillance. Our processors are specifically designed for integration with most state-of-the-art event-based image sensors.
SynSense is working on ultra-low-power always-on key-word and command detection based on Spiking Neural Networks (SNNs). Our technology enables us to process data adjacent to the sensor using novel algorithms specially tailored for our processors. This enables IoT devices powered by our processors to have smart audio capabilities at ultra-low power and latency.
Wearable Devices for Health Monitoring
SynSense is developing hardware solutions for compact neuromorphic spiking resorvior computing. Our solutions enable ultra-low power (sub mW) edge sensory processing and instantaneous detection of anomalies, through continuous monitoring of body signals, like ECG, EMG and EEG from wearable devices.