Accelerating Deep Neural Networks with Analog Memory Devices
Stefano Ambrogio, Pritish Narayanan, et al.
AICAS 2020
We survey progress in the PCM field over the past five years, ranging from large-scale PCM demonstrations to materials improvements for high-Temperature retention and faster switching. Both materials and new cell designs that support lower-power switching are discussed, as well as higher reliability for long cycling endurance. Two paths towards higher density are discussed: Through 3D integration by the combination of PCM and 3D-capable access devices, and through multiple bits per cell, by understanding and managing resistance drift caused by structural relaxation of the amorphous phase. We also briefly survey work in the nascent field of brain-inspired neuromorphic systems that use PCM to implement non-Von Neumann computing.
Stefano Ambrogio, Pritish Narayanan, et al.
AICAS 2020
Huai-Yu Cheng, I. Kuo, et al.
VLSI Technology 2020
Geoffrey W. Burr, Alvaro Padilla, et al.
GLOBECOM 2010
Sangbum Kim, Norma Sosa, et al.
IEEE T-ED