Abstract
For the hardware implementation of artificial intelligence, neuromorphic systems have major advantages in terms of their energy consumption and massively parallel operation compared to conventional computing systems. For general-purpose neuromorphic systems, the on-chip learning of large-scale deep neural networks (DNN) is an essential function. However, compared to a backpropagation algorithm of DNN, an on-chip learning technology, which can be efficiently implemented in hardware without accuracy degradation, has not yet been developed. Consequently, off-chip learning-based neuromorphic systems that perform only inference operations are a promising approach to the first step in the commercialization of neuromorphic systems. To address the limitation of off-chip learning that cannot cope with real-time errors, we proposed on-chip adaptive matching learning (AML). By adding a spare single-layer neural network where an on-chip AML was carried out in parallel to the main neural network, it was possible to implement an adaptive neuromorphic system that can correct errors during real-time applications. For hardware implementation, we proposed a synapse device, synapse array, and neuron circuit. Finally, we conducted a system-level simulation of the adaptive neuromorphic system to verify the feasibility of the proposed on-chip AML.
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