Spiking neural networks implemented with SNAP core operate in parallel, making them significantly faster than software neural networks running on Central Processing Units (CPUs) or Graphics Processing Units (GPUs). SNAP is more energy efficient, enabling SNN to be integrated into portable devices for local processing of sensor data. SNAP based neural networks can respond in real time with low latency, regardless of the neural network size. SNAP implements learning rules in hardware, enabling Autonomous Features Extraction (AFE) directly from input data without need for any software processing .
SNAP based neuromorphic chips can be used in nearly any embedded systems that requires pattern recognition or AFE locally without having to go to cloud based computations. Such systems represent a massive and unlimited potential market, with applications in Smartphones, Internet of Things (IoT), machine to machine (M2M), robotics, gaming, driverless vehicles, drones and air transportation among others.