Current-mode Memristor Crossbars for Neuromemristive Systems
This work addresses hardware implementation challenges for neuromorphic computing, but it is incremental as it builds on existing voltage-mode designs with theoretical mapping and minor modifications.
The paper tackles implementing weight matrices in neuromemristive systems using current-mode memristor crossbars, showing that any voltage-mode weight matrix can be mapped to current-mode with bounded weights and deriving a modified gradient descent rule for training, with simulations on MNIST indicating similar accuracy and defect tolerance but different feature representations.
Motivated by advantages of current-mode design, this brief contribution explores the implementation of weight matrices in neuromemristive systems via current-mode memristor crossbar circuits. After deriving theoretical results for the range and distribution of weights in the current-mode design, it is shown that any weight matrix based on voltage-mode crossbars can be mapped to a current-mode crossbar if the voltage-mode weights are carefully bounded. Then, a modified gradient descent rule is derived for the current-mode design that can be used to perform backpropagation training. Behavioral simulations on the MNIST dataset indicate that both voltage and current-mode designs are able to achieve similar accuracy and have similar defect tolerance. However, analysis of trained weight distributions reveals that current-mode and voltage-mode designs may use different feature representations.