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  • About
    • The mission
    • Contacts
    • People who are involved
    • External Advisory Board
  • Events
    • HPC4AI – Intelligenza Artificiale e Big Data: opportunità e servizi al Politecnico
    • 6th SmartData@Polito Workshop
    • Computing@PoliTO Workshop
  • Seminars
    • Uncertainty damping in the macroscopic forecast of vehicular traffic flow
    • Information Exposure for Consumer IoT Devices: A Multidimensional, Network-Informed Measurement Approach
    • Issues in Spatial Databases and Geographic Information Systems (GIS)
  • Education
    • Courses for PhD students
    • PhD Course: Data Science for Networks
    • PhD Course: Introduction to Random Matrices – Theory and Practice
    • Alta Scuola Politecnica: Winter School 2019
  • Research
    • Publications
    • Current PhD
    • Projects
    • Open Datasets & Libraries
    • Topics
  • Open Positions
    • open position – Assistant Professor with time contract (RTD-A)
    • open position – PostDoc researchers
    • Open position – Technologist
    • open position – PhD student
  • BigData@Polito Cluster
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    • Request BigData@Polito Account
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Home » Publications » Weak-MAC: Arithmetic Relaxation for Dynamic Energy-Accuracy Scaling in ConvNets

Weak-MAC: Arithmetic Relaxation for Dynamic Energy-Accuracy Scaling in ConvNets

Valentino Peluso, Andrea Calimera (2018) Weak-MAC: Arithmetic Relaxation for Dynamic Energy-Accuracy Scaling in ConvNets, In: 2018 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1-5, ISBN: 978-1-5386-4881-0

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