Enabling Bio-Plausible Multi-level STDP using CMOS Neurons with Dendrites and Bistable RRAMs
This work tackles the problem of enabling energy-efficient, large-scale neuromorphic computers for machine learning applications, but it appears incremental as it focuses on overcoming existing device limitations.
The paper addresses the challenges of using resistive random-access memory (RRAM) in neuromorphic computing, which fall short of expected behavior, and proposes 'dendritic learning' as a pathway to overcome these limitations.
Large-scale integration of emerging nanoscale non-volatile memory devices, e.g. resistive random-access memory (RRAM), can enable a new generation of neuromorphic computers that can solve a wide range of machine learning problems. Such hybrid CMOS-RRAM neuromorphic architectures will result in several orders of magnitude reduction in energy consumption at a very small form factor, and herald autonomous learning machines capable of self-adapting to their environment. However, the progress in this area has been impeded from the realization that the actual memory devices fall well short of their expected behavior. In this work, we discuss the challenges associated with these memory devices and their use in neuromorphic computing circuits, and propose pathways to overcome these limitations by introducing 'dendritic learning'.