Wspólnotowy Serwis Informacyjny Badan i Rozwoju - CORDIS

A scalable high speed backplane system for neural network ASICs

The internal architecture of the neural network ASICs in SenseMaker provides connectivity between network blocks and thus enables the composition of recurrent, multi layered neural networks. Therefore and due to the underlying network model, several network blocks may function as one single neural network. Via the digital interface of the chips it is furthermore possible to not only do this cross-linking within one single chip but also to scale it over chip boundaries. This technique brings up high demands on the communication channels between the ASICs: Besides high bandwidth requirements to interface the network blocks the Perception model also requires isochronous network communication.

The distributed backplane system fulfils these requirements. Basic operating unit of the system is the evolution module NATHAN, which basically consists of a Xilinx Virtex-II Pro FPGA, directly connected to the neural network ASIC. Using cutting edge FPGA technology, we can exhaust HAGEN's digital bandwidth, have Multi-GigaBit connectivity, and finally have local CPUs and memory to execute the training software HANNEE. The "distributed" resources of up to 16 NATHAN modules are hosted by a backplane providing the necessary support infrastructure.

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