Artificial Neural Network (ANN) is a type of information processing system based on mimicking the principles of biological brains, and has been broadly applied in application domains such as pattern recognition, automatic control, signal processing, decision support system and artificial intelligence. SNN is a type of biologically-inspired ANN that performs information processing based on discrete-time spikes. It is more biologically realistic than classic ANNs, and can potentially achieve much better performance-to-power ratio.
With the rapid development of the IoT and intelligent hardware systems, a variety of intelligent devices have become pervasive in today's society, providing services and convenience to user’s lives, but they also raise challenges of running complex intelligent algorithms on small devices.
The Darwin NPU aims to provide hardware acceleration of intelligent algorithms on these resource-constrained, low-power, small embedded devices. It has been fabricated by 180nm standard CMOS process, supporting a maximum of 2048 neurons, more than 4million synapses and 15 different possible synaptic delays. Darwin is claimed to be highly configurable, supporting reconfiguration of SNN topology and many parameters of neurons and synapses.
The successful development of Darwin demonstrates the feasibility of real-time execution of SNNs in resource-constrained embedded systems. Its potential applications include intelligent hardware systems, robotics and brain-computer interfaces.
