  The memory physics induced unknown offset of the channel is a critical and
difficult issue to be tackled for many non-volatile memories (NVMs). In this
paper, we first propose novel neural network (NN) detectors by using the
multilayer perceptron (MLP) network and the recurrent neural network (RNN),
which can effectively tackle the unknown offset of the channel. However,
compared with the conventional threshold detector, the NN detectors will incur
a significant delay of the read latency and more power consumption. Therefore,
we further propose a novel dynamic threshold detector (DTD), whose detection
threshold can be derived based on the outputs of the proposed NN detectors. In
this way, the NN-based detection only needs to be invoked when the error
correction code (ECC) decoder fails, or periodically when the system is in the
idle state. Thereafter, the threshold detector will still be adopted by using
the adjusted detection threshold derived base on the outputs of the NN
detector, until a further adjustment of the detection threshold is needed.
Simulation results demonstrate that the proposed DTD based on the RNN detection
can achieve the error performance of the optimum detector, without the prior
knowledge of the channel.
