A Microfluidics Device That Can Perform ANN Computation On Data Stored In DNA

A technical paper titled “Neural network execution using nicked DNA and microfluidics” was published by researchers at University of Minnesota Twin-Cities and Rochester Institute of Technology. Abstract: "DNA has been discussed as a potential medium for data storage. Potentially it could be denser, could consume less energy, and could be more durable than conventional storage media such a... » read more

L-FinFET Neuron For A Highly Scalable Capacitive Neural Network (KAIST)

A new technical paper titled "An Artificial Neuron with a Leaky Fin-Shaped Field-Effect Transistor for a Highly Scalable Capacitive Neural Network" was published by researchers at KAIST (Korea Advanced Institute of Science and Technology). “In commercialized flash memory, tunnelling oxide prevents the trapped charges from escaping for better memory ability. In our proposed FinFET neuron, t... » read more

Artificial Neural Network (ANN)-Based Model To Evaluate The Characteristics of A Nanosheet FET (NSFET)

This new technical paper titled "Machine-Learning-Based Compact Modeling for Sub-3-nm-Node Emerging Transistors" was published by researchers at SungKyunKwan University, Korea. Abstract: "In this paper, we present an artificial neural network (ANN)-based compact model to evaluate the characteristics of a nanosheet field-effect transistor (NSFET), which has been highlighted as a next-generat... » read more

Adaptive NN-Based Root Cause Analysis in Volume Diagnosis for Yield Improvement

Abstract "Root Cause Analysis (RCA) is a critical technology for yield improvement in integrated circuit manufacture. Traditional RCA prefers unsupervised algorithms such as Expectation Maximization based on Bayesian models. However, these methods are severely limited by the weak predictive capability of statistical models and can’t effectively transfer the yield learning experience from old... » read more

Learning properties of ordered and disordered materials from multi-fidelity data

Source: Chen, C., Zuo, Y., Ye, W. et al. Learning properties of ordered and disordered materials from multi-fidelity data. Nat Comput Sci 1, 46–53 (2021). https://doi.org/10.1038/s43588-020-00002-x Abstract: "Predicting the properties of a material from the arrangement of its atoms is a fundamental goal in materials science. While machine learning has emerged in recent years as a n... » read more

Spiking Neural Networks Place Data In Time

Artificial neural networks have found a variety of commercial applications, from facial recognition to recommendation engines. Compute-in-memory accelerators seek to improve the computational efficiency of these networks by helping to overcome the von Neumann bottleneck. But the success of artificial neural networks also highlights their inadequacies. They replicate only a small subset of th... » read more