  Mitigating nonlinear weight update characteristics is one of the main
challenges in designing neural networks based on synaptic devices. This paper
presents a novel weight update method named conditional reverse update scheme
(CRUS) for hardware neural network (HNN) consisting of synaptic devices with
highly nonlinear or abrupt conductance update characteristics. We formulate a
linear optimization method of conductance in synaptic devices to reduce the
average deviation of weight changes from those calculated by the Stochastic
Gradient Rule (SGD) algorithm. We introduce a metric called update noise (UN)
to analyze the training dynamics during training. We then design a weight
update rule that reduces the UN averaged over the training process. The
optimized network achieves >90% accuracy on the MNIST dataset under highly
nonlinear long-term potentiation (LTP) and long-term depression (LTD)
conditions while using inaccurate and infrequent conductance sensing.
Furthermore, the proposed method shows better accuracy than previously reported
nonlinear weight update mitigation techniques under the same hardware
specifications and device conditions. It also exhibits robustness to temporal
variations in conductance updates. We expect our scheme to relieve design
requirements in device and circuit engineering and serve as a practical
technique that can be applied to future HNNs.
