Analog Resistive Switching Devices for Training Deep Neural Networks with the Novel Tiki-Taka Algorithm.

A critical bottleneck for the training of large neural networks (NNs) is communication with off-chip memory. A promising mitigation effort consists of integrating crossbar arrays of analogue memories in the Back-End-Of-Line, to store the NN parameters and efficiently perform the required synaptic operations. The "" algorithm was developed to facilitate NN training in the presence of device nonidealities.

Material to system-level benchmarking of CMOS-integrated RRAM with ultra-fast switching for low power on-chip learning.

Analog hardware-based training provides a promising solution to developing state-of-the-art power-hungry artificial intelligence models. Non-volatile memory hardware such as resistive random access memory (RRAM) has the potential to provide a low power alternative. The training accuracy of analog hardware depends on RRAM switching properties including the number of discrete conductance states and conductance variability.

Roadmap on emerging hardware and technology for machine learning.

Recent progress in artificial intelligence is largely attributed to the rapid development of machine learning, especially in the algorithm and neural network models. However, it is the performance of the hardware, in particular the energy efficiency of a computing system that sets the fundamental limit of the capability of machine learning. Data-centric computing requires a revolution in hardware systems, since traditional digital computers based on transistors and the von Neumann architecture were not purposely designed for neuromorphic computing.

Causes of ferroelectricity in HfO-based thin films: an ab initio perspective.

We present a comprehensive first principles study of doped hafnia in order to understand the formation of ferroelectric orthorhombic[001] grains. Assuming that tetragonal grains are present during the early stages of growth, matching plane analysis shows that tetragonal[100] grains can transform into orthorhombic[001] during thermal annealing when they are laterally confined by other grains. We show that among 0%, 2% and 4% Si doping, 4% doping provides the best conditions for the tetragonal[100] → orthorhombic[001] transformation.