Abstract
Hybrid neural network (H-NN) has been applied in field of remote sensing. To satisfy demands of on-orbit processing that requires high throughput and restriction on power consumption, designing heterogeneous array processor becomes an effective way fulfilling various tasks. A heterogeneous array architecture is proposed to support the hybrid neural network based on characteristics of various computation types among neural network module types and of dynamic computation burden among layers. Firstly, a heterogeneous array structure consisting of different types of PEs is designed, enabling strong flexibility and high throughput. Secondly, multi-level on-chip memory structure and access strategy supporting different access modes are proposed. Thirdly, management strategy for heterogeneous computing array is designed, which combines pipelining and parallel processing to support efficient mapping of diverse hybrid neural networks. The processor has a peak throughput of up to 1.96 TOPS. The implementation on models of AlexNet, LRCN, VGG19-LSTM and CLDNN can achieve the throughput of 1.92 TOPS, 1.89 TOPS, 1.93 TOPS and 1.84 TOPS, respectively. Compared with similar neural network processor that is based on same technology, the throughput of AlexNet model is increased by 76.4%. The peak power consumption of single processor is 824 mW, to which the power restriction of on-orbit AI platform is satisfied.
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Wang, S., Zhang, S., Wang, J., Huang, X. (2020). Towards Energy Efficient Architecture for Spaceborne Neural Networks Computation. In: Qiu, M. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2020. Lecture Notes in Computer Science(), vol 12453. Springer, Cham. https://doi.org/10.1007/978-3-030-60239-0_39
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DOI: https://doi.org/10.1007/978-3-030-60239-0_39
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