Sparse coding with memristor networks pdf

Memristor is a nanoscale circuit element with nonvolatile, binary, multilevel and analog states. Memristor image processor uses sparse coding to see. Both neuroscientists and computer scientists call the process sparse coding. Its conductance resistance plasticity is similar to biological synapses.

Sparse representation of information provides a powerful means to perform feature extraction on highdimensional data and is of broad interest for applications in signal processing, computer vision, object recognition and neurobiology. Adaptive sparse coding based on memristive neural network. Complete patent searching database and patent data analytics services. Then the michigan group took things a step further, programming the memristor array using a braininspired approach called sparse coding to save energy. The goal of sparse coding is solving the following optimization problem. Through hardware implementation of a sparse coding. After the memristor network stabilizes, the reconstructed image can be obtained based on the sparse output neuron activities and the features stored in the crossbar matrix.

A fully hardwarebased memristor convolutional neural network using a hybrid training method achieves an energy efficiency more than two orders of magnitude greater than that of graphics. The setup can measure arrays in sizes of up to 32 rows. Feature extraction and analysis using memristor networks ieee. Sparse representation of information provides a powerful means to perform feature extraction on highdimensional data and is of broad interest. Nanoscale resistive memory memristor based on amorphous films. Lu demonstrated memristor based sparse coding in 2017 on a smaller array. Approaches toward feature extraction and image analysis using memristor networks will be discussed. An overview brian booth sfu machine learning reading group november 12, 20. A component of sparse coding is the ability to exert inhibition among neurons to reconstruct the input 208 using an optimized set of features out of many possible. It was found that moderate levels sa0 faults do not. Sheridan, fuxi cai, chao du, wen ma, zhengya zhang and wei d. The design of a simple, spiking sparse coding algorithm for. Inducing sparse coding and andor grammar from generator network xianglei xing1,2, songchun zhu2.

Other spiking networks optimized for sparse coding have been published. Sparse representation of information provides a powerful means to perform feature extraction on highdimensional data and is of broad interest for applications in signal processing, computer. Lu sparse representation of information provides a powerful means to perform feature extraction on highdimensional data. Pdf memristor devices for neural networks researchgate. Sparse coding is also believed to be a key mechanism by which biological neural systems can efficiently process a large amount of complex sensory data while. Lecun, learning fast approximations of sparse coding. Sparse coding with memristor networks the regents of the.

Network adapt during training following local plasticity rules. Sparse representation of information provides a powerful means to perform feature extraction on highdimensional data and is of broad interest for. Pdf neural network technologies have taken center stage owing to their powerful. Measurement setup figure s1 shows a schematic of the board along with an optical micrograph of the test board with an integrated memristor chip. Fully hardwareimplemented memristor convolutional neural. The final task was a dual layer neural network capable of whats called unsupervised learning. Numerical simulations of memristive networks can be achieved with.