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Pinaki Mazumder co-authors new book: Memristive Computing

The book explores the application of memristive computing to cryptography, signal processing, collective behavior, and more.

ECE faculty design chips for efficient and accessible AI

Faculty specializing in architecture, hardware, and software innovation accelerate machine learning across a range of applications.
Semiconductor Engineering: June 25, 2024

Energy-efficient AI chip

Wei Lu and a large multi-institution team developed an energy efficient AI chip, which is a tunable and stable memristor based on entropy-stabilized oxides.

AI chips could get a sense of time

Timekeeping in the brain is done with neurons that relax at different rates after receiving a signal; now memristors—hardware analogues of neurons—can do that too

Wei Lu named James R. Mellor Professor of Engineering

Lu is internationally renowned for his pioneering efforts in the development and commercialization of novel electrical devices, specifically memristors for memory and logic applications.

Mike Flynn named Fawwaz T. Ulaby Collegiate Professor of Electrical and Computer Engineering

Flynn is one of the world’s premier scholars of analog and mixed-signal integrated circuits and systems, analog-to-digital conversion (ADC), and other interface circuits.

Yongmo Park awarded IBM PhD fellowship to support his research on in-memory computing systems

Park works with Prof. Wei Lu on advancing resistive random-access memory (RRAM, or memristor) devices to accelerate AI computing.

First programmable memristor computer aims to bring AI processing down from the cloud

Circuit elements that store information in their electrical resistances enable a brain-like form of computing, storing and processing information in the same place.

Memory-processing unit (MPU) could bring memristors to the masses

AI, weather forecasting and data science would all benefit from computers that store and process data in the same place. Memristors could be up to the task.

New quick-learning neural network powered by memristors

U-M researchers created a reservoir computing system that reduces training time and improves capacity of similar neural networks.

Next-gen computing inspired by biology

New memristor chips can see patterns over pixels.

Making Memory Smaller, Better, Faster, Stronger

Prof. Wei Lu and former student Dr. Sung Hyun Jo co-founded Crossbar, Inc. to tackle the physical limitations of conventional memory technology.

Artificial synapses could lead to advanced computer memory and machines that mimic biological brains

“This hybrid circuit is a critical advance in developing intelligent machines.”

Wei Lu receives CAREER Award

Lu was awarded an NSF CAREER grant for his research project, which intends to develop unconventional, high-performance memory and logic systems.