2. Conference Papers31

Showing results 21 to 31 of 31

  • Zou, Zhuowen
  • Kim, Yeseong
  • Imani, Farhad
  • Alimohamadi, Haleh
  • Cammarota, Rosario
  • Imani, Mohsen
  • 2021-11-14
  • Zou, Zhuowen. (2021-11-14). Scalable Edge-Based Hyperdimensional Learning System with Brain-Like Neural Adaptation. ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (SC21), 1–15. doi: 10.1145/3458817.3480958
  • IEEE Computer Society
  • View : 89
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  • 2021-12-05
  • Kim, Yeseong. (2021-12-05). CascadeHD: Efficient Many-Class Learning Framework Using Hyperdimensional Computing. Design Automation Conference, 775–780. doi: 10.1109/DAC18074.2021.9586235
  • Institute of Electrical and Electronics Engineers Inc.
  • View : 116
  • Download : 0
  • Kang, Jaeyoung
  • Khaleghi, Behnam
  • Kim, Yeseong
  • Rosing, Tajana
  • 2022-01-18
  • Kang, Jaeyoung. (2022-01-18). XCelHD: An Efficient GPU-Powered Hyperdimensional Computing with Parallelized Training. 27th Asia and South Pacific Design Automation Conference, ASP-DAC 2022, 220–225. doi: 10.1109/ASP-DAC52403.2022.9712549
  • Institute of Electrical and Electronics Engineers Inc.
  • View : 85
  • Download : 0
  • 2022-02-22
  • Park, Jisung. (2022-02-22). DeepSketch: A New Machine Learning-Based Reference Search Technique for Post-Deduplication Delta Compression. USENIX Conference on File and Storage Technologies, 247–263.
  • USENIX Association
  • View : 83
  • Download : 0
  • Ni, Yang
  • Kim, Yeseong
  • Rosing, Tajana
  • Imani, Mohsen
  • 2022-03-17
  • Ni, Yang. (2022-03-17). Algorithm-Hardware Co-Design for Efficient Brain-Inspired Hyperdimensional Learning on Edge. Design Automation and Test in Europe Conference, 292–297. doi: 10.23919/DATE54114.2022.9774524
  • IEEE Council on Electronic Design Automation
  • View : 119
  • Download : 0
  • Ni, Yang
  • Kim, Yeseong
  • Rosing, Tajana
  • Imani, Mohsen
  • 2022-03-22
  • Ni, Yang. (2022-03-22). Online Performance and Power Prediction for Edge TPU via Comprehensive Characterization. Design Automation and Test in Europe Conference, 612–615. doi: 10.23919/DATE54114.2022.9774764
  • IEEE Council on Electronic Design Automation
  • View : 139
  • Download : 0
  • Shim, Jun S.
  • Han, Bogyeong
  • Kim, Yeseong
  • Kim, Jihong
  • 2022-03-23
  • Shim, Jun S. (2022-03-23). DeepPM: Transformer-based Power and Performance Prediction for Energy-Aware Software. Design Automation and Test in Europe Conference, 1491–1496. doi: 10.23919/DATE54114.2022.9774589
  • IEEE Council on Electronic Design Automation
  • View : 130
  • Download : 0
  • Zou, Zhuowen
  • Chen, Hanning
  • Poduval, Prathyush
  • Kim, Yeseong
  • Imani, Mahdi
  • Sadredini, Elaheh
  • Cammarota, Rosario
  • Imani, Mohsen
  • 2022-06-18
  • Zou, Zhuowen. (2022-06-18). BioHD: An Efficient Genome Sequence Search Platform Using HyperDimensional Memorization. ACM/IEEE International Symposium on Computer Architecture, 656–669. doi: 10.1145/3470496.3527422
  • Institute of Electrical and Electronics Engineers Inc.
  • View : 73
  • Download : 0
  • Poduval, Poduval
  • Ni, Yang
  • Kim, Yeseong
  • Ni, Kai
  • Kumar, Raghavan
  • Cammarota, Rossario
  • Imani, Mohsen
  • 2022-07-10
  • Poduval, Poduval. (2022-07-10). Adaptive neural recovery for highly robust brain-like representation. Design Automation Conference, 367–372. doi: 10.1145/3489517.3530659
  • Association for Computing Machinery
  • View : 47
  • Download : 0
  • Imani, Mohsen
  • Zakeri, Ali
  • Chen, Hanning
  • Kim, TaeHyun
  • Poduval, Prathyush
  • Lee, Hyunsei
  • Kim, Yeseong
  • Sadredini, Elaheh
  • Imani, Farhad
  • 2022-07-12
  • Imani, Mohsen. (2022-07-12). Neural Computation for Robust and Holographic Face Detection. Design Automation Conference, 31–36. doi: 10.1145/3489517.3530653
  • Association for Computing Machinery
  • View : 62
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  • Park, Jongho
  • Kwon, Hyuk-jun
  • Kim, Seowoo
  • Lee, Junyoung
  • Ha, Minho
  • Lim, Euicheol
  • Imani, Mohsen
  • Kim, Yeseong
  • 2022-07-14
  • Park, Jongho. (2022-07-14). QuiltNet: Efficient Deep Learning Inference on Multi-Chip Accelerators Using Model Partitioning. Design Automation Conference, 1159–1164. doi: 10.1145/3489517.3530589
  • Association for Computing Machinery
  • View : 130
  • Download : 0
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