Neuromorphic Solutions


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.

SynSense provides custom-tailored, ultra-low-power silicon design solutions for industrial and consumer machine-learning inference applications. As a “full-stack” neuromorphic engineering company, SynSense delivers complete neuromorphic intelligence solutions, including custom IP, 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.

Auditory Processing

SynSense has implemented 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.

Bio-signal Processing

SynSense has developed hardware solutions for compact neuromorphic bio-signal processing. 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.

Multimodal Signal Processing

SynSense has long experience in designing high efficiency core architectures which mimic the human brain. Combining bio- inspired spiking neural networks and signal processing approaches, we are building a new generation of multimodal signal processors that enables real-time, low power AI tasks in edge devices.

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