16 citations · 27 across the 7 of their papers we have counts for
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cs.AR2022
Sustainable AI Processing at the Edge
Sébastien Ollivier, Sheng Li, Yue Tang +5
Edge computing is a popular target for accelerating machine learning algorithms supporting mobile devices without requiring the communication latencies to handle them in the cloud.…
cs.LG2022★ 2 cited
H2H: Heterogeneous Model to Heterogeneous System Mapping with Computation and Communication Awareness
Xinyi Zhang, Cong Hao, Peipei Zhou +2
The complex nature of real-world problems calls for heterogeneity in both machine learning (ML) models and hardware systems. The heterogeneity in ML models comes from multi-sensor…
cs.LG2022★ 1 cited
EF-Train: Enable Efficient On-device CNN Training on FPGA Through Data Reshaping for Online Adaptation or Personalization
Yue Tang, Xinyi Zhang, Peipei Zhou +1
Conventionally, DNN models are trained once in the cloud and deployed in edge devices such as cars, robots, or unmanned aerial vehicles (UAVs) for real-time inference. However, the…