CIMulator: A Comprehensive Simulation Platform for Computing-In-Memory Circuit Macros with Low Bit-Width and Real Memory Materials

This paper presents a simulation platform, namely CIMulator, for quantifying the efficacy of various synaptic devices in neuromorphic accelerators for different neural network architectures. Nonvolatile memory devices, such as resistive random-access memory, ferroelectric field-effect transistor, and volatile static random-access memory devices, can be selected as synaptic devices. A multilayer perceptron and convolutional neural networks (CNNs), such as LeNet-5, VGG-16, and a custom CNN named C4W-1, are simulated to evaluate the effects of these synaptic devices on the training and inference outcomes. The dataset used in the simulations are MNIST, CIFAR-10, and a white blood cell dataset. By applying batch normalization and appropriate optimizers in the training phase, neuromorphic systems with very low-bit-width or binary weights could achieve high pattern recognition rates that approach software-based CNN accuracy. We also introduce spiking neural networks with RRAM-based synaptic devices for the recognition of MNIST handwritten digits.

Alleviation of Temperature Variation Induced Accuracy Degradation in Ferroelectric FinFET Based Neural Network

This paper reports the impacts of temperature variation on the inference accuracy of pre-trained all-ferroelectric FinFET deep neural networks, along with plausible design techniques to abate these impacts. We adopted a pre-trained artificial neural network (NN) with 96.4% inference accuracy on the MNIST dataset as the baseline. As an aftermath of temperature change, the conductance drift of a programmed cell was captured by a compact model over a wide range of gate bias. We observe a significant inference accuracy degradation in the analog neural network at 233 K for a NN trained at 300 K. Finally, we deployed binary neural networks with "read voltage" optimization to ensure immunity of NN to accuracy degradation under temperature variation, maintaining an inference accuracy 96.1%