Details

Autor(en) / Beteiligte

• Chen, Xiaobai
• Hu, Qiurun
• Xiao, Fu
• Yin, Jieming

Titel

CompoundEye: A 0.24-4.17 TOPS Scalable Multi-Node DNN Processor for Image Recognition

Ist Teil von

• 2023 IEEE International Symposium on Circuits and Systems (ISCAS), 2023, p.1-5

Ort / Verlag

IEEE

Erscheinungsjahr

2023

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• This paper proposes a scalable DNN processor that can be flexibly reconfigured to maximize inference efficiency on a wide range of DNN models. The processor consists of 18 computing nodes with various precision modes support. To improve the computation throughput, we propose a sub-image parallelization strategy, where the original input image is divided into multiple sub-images and computed on multiple nodes in parallel. In addition, the cross-layer pipeline is implemented to improve resource utilization. The proposed processor is implemented in 28nm CMOS technology and achieves a peak performance of 4.17 TOPS and an energy efficiency of 2.08 TOPS/W.