The team will look at two architectures used in neuromorphic computing: static RAM, the current state-of-the-art methodology for designing neuromorphic platforms, and resistive RAM, a low energy resistive memory.

SRAM architectures improve on conventional architectures, but they use a lot of space and power and are known to be weak to ionizing radiation. RRAM is an emerging nanotechnology that is a proposed replacement for SRAM-based synapses as they’re smaller and more power efficient.

“RRAM cells have inherent electrical characteristics that are very similar to biological synapses — connections grow within the devices that become stronger the more they are stimulated, which is quite similar to what happens in the brain as it learns,” Kozicki said. “These cells are also the smallest devices that can be made in an integrated circuit, much smaller than a single transistor, and unlike regular transistors they can be stacked on top of each other, leading to huge synaptic density.”

The team’s preliminary work on their RRAM variant, the Programmable Metallization Cell, shows promise for radiation hardness.

“The Programmable Metallization Cell is extremely radiation-tolerant, so we have high confidence that rad-hard (radiation-hardened) systems based on the technology will be possible,” Kozicki said.

Barnaby’s team will study each architecture at the device, circuit and system levels in hardware and through simulation. With the help of Sandia National Laboratories’ facilities, they will expose them to different types of radiation, observe the effects and create models that help explain why those effects occurred.

Understanding radiation effects

In organic tissue, high-energy radiation is a common contributor to cell mutations. For example, gamma radiation, which comes from high-energy photons, is a high-frequency, short wavelength about the size of a DNA molecule. When DNA molecules and gamma radiation waves clash, it causes mutations in our DNA.

Radiation is also a concern for electronics, Barnaby says. Exposure to gamma rays, high energy charged particles like electrons and ions and even high-energy neutrons can cause electronic systems to fail. For the RRAM devices the team plans to use in their novel neural systems, Barnaby believes neutrons and heavy ions may be a particularly significant threat

“In electronics, neutrons and heavy ions can reconfigure the structure of the material,” Barnaby said. “The energy radiation absorbed from heavy ions can also create erroneous signals that can change the synaptic strength artificially. This can lead to errors in neuro-processing.”

Structural reorganization and transient ion effects are two potential radiation threats the team has identified so far, and will look for other primary radiation threats to neuromorphic computing systems.

They’ll look at chip-scale and system-level effects from transient damage, which includes single and multiple instances of radiation damage, and effects from cumulative radiation damage over time. The team will also try to understand which stage of a neuro-inspired algorithm’s execution is most vulnerable, and how radiation-caused errors propagate through the layers of a neural computing network.

Developing electronics for harsh environments

From their testing, Barnaby’s team will determine which hardware platform has the best prospects for radiation hardness and is the most beneficial for applications where radiation exposure is likely.

“This work may enable major breakthroughs in low-power co-processors for remote sensing and data analysis,” Barnaby said. “Neuromorphic co-processors could benefit many Department of Defense strategic and space mission applications that operate in radiation environments and are limited by traditional von Neumann computing, such as machine learning, computer vision or control of non-linear dynamic systems — object recognition by drones, for example.”

By better understanding the basic radiation effects on mechanisms in neuro-processing elements and virtual synapses, the team hopes to speed the development of radiation-hardened neuromorphic computing architectures.

“This will allow a quicker adoption of the neuromorphic systems for use in radiation environments and shorten the timeline for developing necessary hardening techniques for neuromorphic systems,” Barnaby said.

Monique Clement

Communications specialist, Ira A. Fulton Schools of Engineering