Abstract
Multiple neuromorphic systems use spiking neural networks (SNNs) to perform computation in a way that is inspired by concepts learned about the human brain. SNNs are artificial networks made up of neurons that fire a pulse, or spike, once the accumulated value of the inputs to the neuron exceeds a threshold. One of the most challenging parts of designing neuromorphic hardware is handling the vast degree of connectivity that neurons have with each other in the form of synaptic connections. This paper analyzes the neuromorphic systems Neurogrid, Braindrop, SpiNNaker, BrainScaleS, TrueNorth, Loihi, Darwin, and Dynap-SEL; and discusses the design of large scale spiking communication networks used in such systems. In particular, this paper looks at how each of these systems solved the challenges of forming packets with spiking information and how these packets are routed within the system. The routing of packets is analyzed at two scales: How the packets should be routed when traveling a short distance, and how the packets should be routed over longer global connections. Additional topics, such as the use of asynchronous circuits, robustness in communication, connection with a host machine, and network synchronization are also covered.
Highlights
The human brain is an amazing computational machine, with many properties that surpass the capabilities of modern supercomputers
Each neuron checks the tag against the tags stored in a Content Addressable Memory (CAM) to see if the neuron is connected to the source neuron with the same tag. This use of tags allows the tag addresses to be shared among source and destination neurons from different clusters, which results in there being less memory required to store the connection information
With any global communication network on the same scale as the ones found in neuromorphic processors, robustness of the system is of key importance as failures are almost guaranteed to occur
Summary
YOUNG , (Student Member, IEEE), MARK E.
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