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pro vyhledávání: '"Katherine Spoon"'
Autor:
Pritish Narayanan, Katherine Spoon, Scott C. Lewis, Hsinyu Tsai, An Chen, Alexander Friz, Kohji Hosokawa, Stefano Ambrogio, Andrea Fasoli, Charles Mackin, Geoffrey W. Burr, Jose Luquin
Publikováno v:
ISCAS
By performing parallelized multiply-accumulate operations in the analog domain at the location of weight data, crossbar-array "tiles" of analog non-volatile memory (NVM) devices can potentially accelerate the forward-inference of deep neural networks
Autor:
Geoffrey W. Burr, Stefano Ambrogio, Charles Mackin, Alexander Friz, Andrea Fasoli, Hsinyu Tsai, An Chen, Katherine Spoon, Pritish Narayanan
Publikováno v:
2020 International Symposium on VLSI Technology, Systems and Applications (VLSI-TSA).
Acceleration of Deep Neural Networks (DNNs) inference using non-volatile memory (NVM) arrays, such as PhaseChange Memory (PCM), shows promising advantages with respect to digital implementations, in terms of energy efficiency and speed. By exploiting
Autor:
Andrea Fasoli, An Chen, Hsinyu Tsai, Geoffrey W. Burr, Alexander Friz, Katherine Spoon, Stefano Ambrogio, Pritish Narayanan, Charles Mackin
Publikováno v:
2020 4th IEEE Electron Devices Technology & Manufacturing Conference (EDTM).
Non-volatile analog memory and in-memory computing have great potential to enable high-performance Deep Neural Network (DNN) inference accelerators with significantly better performance and efficiency than digital processors. Analog Phase Change Memo
Autor:
Stefano Ambrogio, An Chen, Andrea Fasoli, Pritish Narayanan, Alexander Friz, Hsinyu Tsai, Charles Mackin, Geoffrey W. Burr, Katherine Spoon
Publikováno v:
Scopus-Elsevier
ISCAS
ISCAS
Neuromorphic computation based on analog nonvolatile memories (NVMs) holds great promise to improve Deep Neural Networks inference performance. In virtue of an architecture that executes the computation at the location of the stored weight data, rema
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