What if next-generation computing systems were able to adopt the human brain’s information processing capacity and energy efficiency?
Researchers at Rochester Institute of Technology have begun to answer that question to improve today’s computing capacity using neuromorphic engineering—neuro-inspired computing that combines elements of neuroscience, nanotechnology and intelligent system design. The goal is to re-design computing systems modeled after the brain’s biological processing capabilities to be able to assess and integrate ever-larger quantities of data.
“We are designing a new generation of computing systems, inspired by the operating principles of the human brain,” said Dhireesha Kudithipudi, associate professor of computer engineering in RIT’s Kate Gleason College of Engineering. “The consensus among several leading neuroscientists is that the neocortex is the brain’s primary processing engine, so many of our current projects take inspiration from the principles underlying neocortical processing. We are also exploring the role of subcortical structures in facilitating communication and complex computations within the brain.”
Kudithipudi and members of the NanoComputing Research Lab are pursuing several funded research projects related to neuromorphic engineering, defined as an interdisciplinary approach to developing computing infrastructure based on how the human brain performs its complex functions. Their research approach is multi-tiered spanning neural network architectures, neuromemristive devices, digital/analog circuits, neuropsychology, and development/adaptation of machine learning algorithms for hardware. The team has demonstrated designs for applications such as real-time environmental sensing, load forecasting for smart grids, speech recognition, anomaly detection and object classification.