Scientists have successfully developed a 'Darwin' neural chip, a kind of information processing system that mimics the principles of biological brains, an advance that could lead to the development of artificial intelligence systems and Internet-of-Things.
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.
Spiking Neural Network (SNN) is a 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-power ratio.
Researchers from Zhejiang University and Hangzhou Dianzi University in China successfully developed the Darwin Neural Processing Unit (NPU), a neuromorphic hardware co-processor based on Spiking Neural Networks, fabricated by standard CMOS (complementary metal-oxide semiconductor) technology.
With the rapid development of the Internet-of-Things and intelligent hardware systems, a variety of intelligent devices are pervasive in today's society, providing many services and convenience to people's lives, but they also raise challenges of running complex intelligent algorithms on small devices.
The research group led by De Ma from Hangzhou Dianzi university and Xiaolei Zhu from Zhejiang university has developed a co-processor named as Darwin.
The Darwin NPU aims to provide hardware acceleration of intelligent algorithms, with target application domain of resource-constrained, low-power small embedded devices.
It has been fabricated by 180nm standard CMOS process, supporting a maximum of 2048 neurons, more than 4 million synapses and 15 different possible synaptic delays.
It is 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 Spiking Neural Networks in resource-constrained embedded systems.
It supports flexible configuration of a multitude of parameters of the neural network, hence it can be used to implement different functionalities as configured by the user.
Its potential applications include intelligent hardware systems, robotics, brain-computer interfaces, and others.
Since it uses spikes for information processing and transmission, similar to biological neural networks, it may be suitable for analysis and processing of biological spiking neural signals, and building brain-computer interface systems by interfacing with animal or human brains.
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