Ziksa: On-Chip Learning Accelerator with Memristor Crossbars for Multilevel Neural Networks

Memristor crossbars support efficient realizations of spiking and non-spiking neural networks designs. In most of these designs off-chip/ex-situ training is used to set/update the state of the memrisitve devices. However, there is a growing need to design an efficient on-chip/in-situ learning for mobile autonomous systems. In this research, we propose an on-chip learning accelerator, known as Ziksa, that is integrated with the memristor crossbars. We demonstrate how regression and back-propagation in multi-level networks can be realized through Ziksa. The proposed accelerator is evaluated on a fabricated TiN-TaOx-TaTiN memristor crossbar. A 3-layer feedforward network was tested using Ziksa for classification. An accuracy of 95.3% was achieved on Wisconsin breast cancer dataset. The proposed learning accelerator can be envisioned as a core building block in a wide-range of cognitive algorithms that rely on on-chip online learning.