By Ed Korczynski, Sr. Technical Editor
Knowm Inc. (www.knowm.org), a start-up pioneering next-generation advanced computing architectures and technology, recently announced the availability of two new variations of memristors targeting different neuromorphic applications. The company also announced raw device data available for purchase to help researchers develop and improve memristor models. These new Knowm offerings enable the next step in the R&D of radically new chips for pattern-recognition, machine-learning, and artificial intelligence (AI) in general.
There is general consensus between industry and academia and government that future improvements in computing are now severely limited by the amount of energy it takes to use Von Neumann architectures. Consequently, the US Whitehouse has issued a grand challenge with the Energy-Efficient Computing: from Devices to Architectures (E2CDA) program (http://www.nsf.gov/pubs/2016/nsf16526/nsf16526.htm) actively soliciting proposals through March 28, 2016.
The Figure shows a schematic cross-section of Knowm’s memristor devices—with Tin (Sn) and Chromium (Cr) metal layers as the new options to tungsten (W)—along with the device I/V curves for each. “They differ in their activation threshold,” explained Knowm CEO and co-founder Alex Nugent in an exclusive interview with Solid State Technology. “As the activation thresholds become smaller you get reduced data retention, but higher cycle endurance. As that threshold increases you have to dissipate more energy per event, and the more energy you dissipate the faster it will burn-out.” Knowm’s two new memristors, as well as the company’s previously announced device, are now available as unpackaged raw dice with masks designed for research probe stations.
Knowm is working on the simultaneous co-optimization of the entire “stack” from memristors to circuit architectures to application-specific algorithms. “The potential of memristors is so huge that we are seeing exponential growth in the literature, a sort of gold rush as engineers race to design new circuits and re-envision old circuits,” commented Knowm CEO and co-founder Alex Nugent. “The problem is that in the race to publish, circuit designers are adopting models that do not adequately describe real devices.” Knowm’s raw data includes AC, DC, pulse response, and retention for different memristors.
Additional memristors are being developed by Knowm’s R&D lab partner Dr. Kris Campbell of Boise State University (http://coen.boisestate.edu/kriscampbell/), using different metal layers to achieve different activation thresholds beyond the three shown to date. “She has discovered an algorithm for creating memristors along this dimension,” said Nugent. “From a physics perspective it makes sense that there would be devices with high cycle endurance but reduced data retention.”
“In the future what I image is a single chip with multiple memristors on it. Some will be volatile and very fast, while others will be slow,” continued Nugent. “Just like analog design today uses different capacitors, future neuromophic chips would likely use memristors optimized for different changes in adaptation threshhold. If you think about memristors as fundamental elements—as per Leon Chua (https://en.wikipedia.org/wiki/Leon_O._Chua)—then it makes sense that we’ll need different memristors.”
The applications spaces for these devices have intrinsically different requirements for speed and retention. For example, to exploit these devices for pattern recognition and/or anomaly detection (keeping track of confidence in making temporal predictions) it seems best to choose relatively high activation thresholds because the number of operations is unlikely to burn-out devices. Conversely, for circuits that constantly solve optimization problems the best memristors would require low burn-out and thus low activation thresholds. However, analog applications are generally problematic because the existing memristors leak current, such that stored values degrade over time.
Knowm is shipping devices today, mostly to university researchers, and has tested thousands of devices itself. The Knowm memristors can be fabricated at <500°C using industry-standard unit-process steps, allowing for eventual integration with silicon CMOS “back-end” metallization layers. While still in early R&D, this technology could provide much of the foundation for post-Moore’s-Law silicon ICs.