Spiking Neural Network Development

Deploy AI inference at the edge with power consumption measured in milliwatts, not watts. Our SNN designs leverage the sparse, event-driven computation of neuromorphic processors like Intel Loihi to enable battery-powered devices that operate for years, not days.

Achieve sub-millisecond, predictable latency for time-critical applications. Unlike traditional deep neural networks with variable batch processing, our spiking neural networks process sensory events as they occur, essential for robotics, industrial control, and high-frequency signal analysis.

Integrate directly with novel, efficient sensors like event-based cameras and dynamic vision sensors (DVS). Our SNNs are inherently suited to processing sparse, asynchronous data streams, turning raw sensor events into actionable intelligence without wasteful frame-based processing.

Move complex pattern recognition and sensory fusion directly to the endpoint. By performing intelligent processing locally with SNNs, you eliminate constant bandwidth costs, reduce latency, and enhance data privacy—critical for applications in remote industrial sites or consumer devices.

Build on a computational paradigm aligned with next-generation hardware. Our development with frameworks like Nengo and Lava ensures your AI models are optimized not just for today's GPUs, but for the coming wave of neuromorphic chips, protecting your R&D investment.

Accelerate from concept to deployed system with our team's deep experience in neuromorphic software-hardware co-design. We handle the full stack—from SNN model design and training to deployment and performance tuning on target hardware—ensuring a production-ready outcome.

Deploy always-on SNN sensors that analyze vibration, acoustic, and thermal patterns from machinery, predicting failures weeks in advance while operating for years on a single battery. This reduces unplanned downtime by over 40% and eliminates manual inspection rounds.

Implement perception and navigation systems for drones and mobile robots using event-based cameras and SNNs. Achieve deterministic sub-10ms object detection and avoidance, enabling safe operation in dynamic warehouses and outdoor environments without GPU power budgets.

Fuse inputs from LiDAR, radar, and event cameras using SNNs for advanced driver-assistance systems (ADAS) and smart city infrastructure. Process multimodal streams synchronously to create a coherent environmental model with 60% lower energy consumption than conventional CNN approaches.

Develop wearable and implantable devices for continuous health signal processing (ECG, EEG, EMG). SNNs enable real-time anomaly detection for cardiac events or neurological episodes while consuming less than 1mW, making perpetual patient monitoring clinically and commercially viable.

Build systems for real-time RF signal classification and spectrum awareness in contested environments. SNNs process raw RF waveforms directly, enabling adaptive electronic warfare and cognitive radio applications with unparalleled efficiency on Size, Weight, and Power (SWaP)-constrained platforms.

Replace power-hungry continuous video streaming with event-based cameras and SNNs for perimeter security and traffic monitoring. This architecture triggers analysis only on pixel changes, reducing data bandwidth by 95% and enabling privacy-preserving, long-duration deployment in remote areas.