The human brain contains roughly 100 billion neurons. Each operates at approximately 150 Hz, far slower than modern digital processors, suggesting that the brain's computational strength stems from its massively parallel architecture rather than sheer processing speed. Deep machine learning (DML) has recently emerged as a promising framework for mimicking the information representation capabilities of the brain. Inspired by discoveries in neurobiology, hidden layers of deep learning systems encode hierarchically distributed representations of complex inputs. However, the fundamental mismatch between a highly parallel architecture and the serial structure of conventional processors limits the scalability of software-based DML systems. By fully leveraging the computational power of individual transistors, analog neuromorphic circuits achieve much greater density and energy efficiency than digital technology. The goal of this research is to use neuromorphic analog computational elements to enable scalable deep learning systems.
The proposed research offers the potential to revolutionize the design and utilization of large-scale deep machine learning systems with applications to real-world, complex, high-dimensional pattern recognition problems. In particular, the study of floating-gate circuitry for realizing such systems is expected to have broad impact on the field of neuromorphic engineering. The long-range impact of compact, power-efficient learning systems will be profound, with benefits to society ranging from micro-scale health-monitoring sensors capable of learning from their own observations to large-scale autonomous processing of multimedia data. The interdisciplinary nature of the project will provide an opportunity for graduate and undergraduate students to work at the intersection of different specializations and provide a unique platform on which to study the emergent properties of complex biologically-inspired systems.
