Energy efficiency of machine learning in embedded systems using neuromorphic hardware

HIGHLIGHTS

• who: Minseon Kang et al. from the Department of Computer Science and Engineering, Incheon National University, Incheon, Korea have published the research work: Energy Efficiency of Machine Learning in Embedded Systems Using Neuromorphic Hardware, in the Journal: (JOURNAL)
• what: The authors implemented a pedestrian image detection system on an embedded device using a commercially available neuromorphic chip NM500 which is based on NeuroMem technology.
• how: For these three different configurations (neuromorphic chips GPUs and CPUs) of embedded systems the processing time and power consumption for learning and classification are measured and the energy . . .
  

   

  

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