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First Programmable Memristor Computer Aims to Bring AI Processing Down from the Cloud
July 18, 2019 | Michigan State UniversityEstimated reading time: 4 minutes
To build the first programmable memristor computer, Lu’s team worked with associate professor Zhengya Zhang and professor Michael Flynn, both of electrical and computer engineering at U-M, to design a chip that could integrate the memristor array with all the other elements needed to program and run it. Those components included a conventional digital processor and communication channels, as well as digital/analog converters to serve as interpreters between the analog memristor array and the rest of the computer.
Lu’s team then integrated the memristor array directly on the chip at U-M’s Lurie Nanofabrication Facility. They also developed software to map machine learning algorithms onto the matrix-like structure of the memristor array.
The team demonstrated the device with three bread-and-butter machine learning algorithms:
- Perceptron, which is used to classify information. They were able to identify imperfect Greek letters with 100% accuracy
- Sparse coding, which compresses and categorizes data, particularly images. The computer was able to find the most efficient way to reconstruct images in a set and identified patterns with 100% accuracy
- Two-layer neural network, designed to find patterns in complex data. This two-layer network found commonalities and differentiating factors in breast cancer screening data and then classified each case as malignant or benign with 94.6% accuracy.
There are challenges in scaling up for commercial use—memristors can’t yet be made as identical as they need to be and the information stored in the array isn’t entirely reliable because it runs on analog’s continuum rather than the digital either/or. These are future directions of Lu’s group.
Lu plans to commercialize this technology. The study is titled, “A fully integrated reprogrammable memristor–CMOS system for efficient multiply–accumulate operations.” The research is funded by the Defense Advanced Research Projects Agency, the center for Applications Driving Architectures (ADA), and the National Science Foundation.
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