11 citations · 11 across the 2 of their papers we have counts for
5 papers
Measurement of onset of structural relaxation in melt-quenched phase change materials
Benedikt Kersting, Syed Ghazi Sarwat, Manuel Le Gallo +5
Chalcogenide phase change materials enable non-volatile, low-latency storage-class memory. They are also being explored for new forms of computing such as neuromorphic and in-memor…
Accurate Emulation of Memristive Crossbar Arrays for In-Memory Computing
Anastasios Petropoulos, Irem Boybat, Manuel Le Gallo +3
In-memory computing is an emerging non-von Neumann computing paradigm where certain computational tasks are performed in memory by exploiting the physical attributes of the memory…
ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning
Vinay Joshi, Geethan Karunaratne, Manuel Le Gallo +5
Deep neural networks (DNNs) have surpassed human-level accuracy in a variety of cognitive tasks but at the cost of significant memory/time requirements in DNN training. This limits…
Accurate deep neural network inference using computational phase-change memory
Vinay Joshi, Manuel Le Gallo, Simon Haefeli +7
In-memory computing is a promising non-von Neumann approach for making energy-efficient deep learning inference hardware. Crossbar arrays of resistive memory devices can be used to…
Mixed-precision training of deep neural networks using computational memory
Nandakumar S. R., Manuel Le Gallo, Irem Boybat +3
Deep neural networks have revolutionized the field of machine learning by providing unprecedented human-like performance in solving many real-world problems such as image and speec…