2017/07/17 by Cory Merkel, Merkel, Cory
Computer Science · Engineering · Mathematics · Neuroscience · #Advanced Memory and Neural Computing #CCD and CMOS Imaging Sensors #Neuroscience and Neural Engineering #cs.ET #stat.ML
paper · pdf · doi:10.48550/arxiv.1707.05316
arxiv created 2017/07/17 · arxiv updated 2017/07/19
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.