Mitigating Non-idealities of Memristive-based Artificial Neural Networks - an Algorithmic Approach

The computing power demands to run artificial neural networks (ANNs) are increasing at rates much greater than improvements made with current CMOS-based technologies. The demand has contributed to a need for novel paradigms, including memristor-based accelerators. This work explores two algorithmic approaches to mitigate non-idealities inherent in most memristor-based systems. The first is to apply a concept of committee machines during inference, and the second is nonideality-aware training of memristor-based ANNs.