Brain Inspired Networks for Multifunctional Intelligent Systems in Aerial Vehicles
Abstract
Computers have led to an information revolution and artificial intelligent systems that simulate the learning functions of the human brain. The world’s fastest supercomputer, Summit, may have a computing capacity comparable to that of the human brain. However, Summit consumes the equivalent power of 7000 homes (~15 MW), and the brain only consumes a power of a light bulb (~20 W). Computers execute algorithms on physically separated logic and memory units in digital serial mode, which fundamentally restrains computers from handling “big data” efficiently in complex dynamic environments, and limits the developments of emerging intelligent systems such as self piloted unmanned aerial vehicles (UAVs). By contrast, the brain simultaneously processes and learns from “big data” via trillions of synapses and neurons in analog parallel mode, and facilitates parallel processing and real time learning with an energy efficiency more than five orders of magnitude superior to that of the supercomputer. The proposed study intends to perform research on devices including synaptic resistors (synstors), memory resistors (memristors), and neuristors to emulate the analog short and long term memory, convolutional signal processing, and correlative learning functions of synapses, and the nonlinear dynamic functions of neurons.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 14, 2022
- Source ID
- FA95501910213
Entities
People
- Yong Chen
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of California, Los Angeles