Defect-Tolerant Memristor Crossbar Circuits for Local Learning Neural Networks.

Local learning algorithms, such as Equilibrium Propagation (EP), have emerged as alternatives to global learning methods like backpropagation for training neural networks. EP offers the potential for more energy-efficient hardware implementation by utilizing only local neuron information for weight updates. However, the practical implementation of EP using memristor-based circuits has significant challenges due to the immature fabrication processes of memristors, resulting in defects and variability issues.

Memristor-CMOS Hybrid Circuits Implementing Event-Driven Neural Networks for Dynamic Vision Sensor Camera.

For processing streaming events from a Dynamic Vision Sensor camera, two types of neural networks can be considered. One are spiking neural networks, where simple spike-based computation is suitable for low-power consumption, but the discontinuity in spikes can make the training complicated in terms of hardware. The other one are digital Complementary Metal Oxide Semiconductor (CMOS)-based neural networks that can be trained directly using the normal backpropagation algorithm.

Memristor Crossbar Circuits Implementing Equilibrium Propagation for On-Device Learning.

Equilibrium propagation (EP) has been proposed recently as a new neural network training algorithm based on a local learning concept, where only local information is used to calculate the weight update of the neural network. Despite the advantages of local learning, numerical iteration for solving the EP dynamic equations makes the EP algorithm less practical for realizing edge intelligence hardware. Some analog circuits have been suggested to solve the EP dynamic equations physically, not numerically, using the original EP algorithm.

Area-Efficient Mapping of Convolutional Neural Networks to Memristor Crossbars Using Sub-Image Partitioning.

Memristor crossbars can be very useful for realizing edge-intelligence hardware, because the neural networks implemented by memristor crossbars can save significantly more computing energy and layout area than the conventional CMOS (complementary metal-oxide-semiconductor) digital circuits. One of the important operations used in neural networks is convolution. For performing the convolution by memristor crossbars, the full image should be partitioned into several sub-images.

Effect of Zeolite Used for Sediment Remediation on Sulfide, Phosphate, and Nitrogen Control in a Microcosm.

Eutrophication is an emerging worldwide issue concerning the excessive accumulation of various pollutants in sediments, owing to the release of industrial or household wastewaters to coastal areas. The coastal sediment of Goseong Bay in the Republic of Korea is organically enriched with pollutants, including heavy metals, sulfide, phosphate, and ammonia. Microbial remediation and capping techniques have been suggested as effective routes for sediment remediation.

Synapse-Neuron-Aware Training Scheme of Defect-Tolerant Neural Networks with Defective Memristor Crossbars.

To overcome the limitations of CMOS digital systems, emerging computing circuits such as memristor crossbars have been investigated as potential candidates for significantly increasing the speed and energy efficiency of next-generation computing systems, which are required for implementing future AI hardware. Unfortunately, manufacturing yield still remains a serious challenge in adopting memristor-based computing systems due to the limitations of immature fabrication technology. To compensate for malfunction of neural networks caused from the fabrication-related defects, a new crossbar training scheme combining the synapse-aware with the neuron-aware together is proposed in this paper, for optimizing the defect map size and the neural network's performance simultaneously.