论文标题
基于28nm HKMG FEFET的突触核
Variation Aware Training of Hybrid Precision Neural Networks with 28nm HKMG FeFET Based Synaptic Core
论文作者
论文摘要
这项工作提出了一个混合精确的神经网络训练框架，其基于ENVM的计算存储单元执行加权总和操作和另一个SRAM单元，该单元在背部传播过程中存储重量更新的误差，以及所需的脉冲数以更新硬件中的权重。具有28 nm铁电力FET（FEFET）的基于MLP的神经网络的混合训练算法作为突触设备，在存在设备和周期变化的情况下，可以实现高达95％的推理精度。该体系结构主要是使用FEFET设备的行为或宏观模型评估的，具有实验校准的设备变化，与浮点实现相比，我们已经实现了准确性。
This work proposes a hybrid-precision neural network training framework with an eNVM based computational memory unit executing the weighted sum operation and another SRAM unit, which stores the error in weight update during back propagation and the required number of pulses to update the weights in the hardware. The hybrid training algorithm for MLP based neural network with 28 nm ferroelectric FET (FeFET) as synaptic devices achieves inference accuracy up to 95% in presence of device and cycle variations. The architecture is primarily evaluated using behavioral or macro-model of FeFET devices with experimentally calibrated device variations and we have achieved accuracies compared to floating-point implementations.
