Pattern classification by memristive crossbar circuits using ex situ and in situ training

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Hardware implementation of memristor-based artificial neural networks (2024, unknown): “Ex situ training has the advantage of requiring little/no circuit overhead to perform quick tests of the classi ﬁ cation performance of the network, and hasmade possible to evaluate the performanceof home-made memristive crossbar-arrays 93,188 .”
Organic electronics for neuromorphic computing (2018, background): “The work of Strukov and colleagues has demonstrated that neural network algorithms, which traditionally have been implemented on a software level, could indeed be embedded in hardware itself, thus emulating the function and efficiency of the brain on a compact chip 17,18 .”
PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference (2019, background): “data movement costs relative to compute [49]. To overcome this limitation, memristor crossbars can store a matrix with high storage density and perform MVM operations with very low energy and latency [5, 13, 52, 87, 98, 116]. Each crosspoint in the crossbar stores a multi-bit value in one memristor ”
ISAAC: A Convolutional Neural Network Accelerator with In-Situ Analog Arithmetic in Crossbars (2016, background): “This is therefore an example of in-situ computing [2], [30], [53].”