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

• URL: http://arxiv.org/abs/2306.14649v1
• Date: Mon, 26 Jun 2023 12:36:07 GMT
• Title: CIMulator: A Comprehensive Simulation Platform for Computing-In-Memory Circuit Macros with Low Bit-Width and Real Memory Materials
• Authors: Hoang-Hiep Le, Md. Aftab Baig, Wei-Chen Hong, Cheng-Hsien Tsai, Cheng-Jui Yeh, Fu-Xiang Liang, I-Ting Huang, Wei-Tzu Tsai, Ting-Yin Cheng, Sourav De, Nan-Yow Chen, Wen-Jay Lee, Ing-Chao Lin, Da-Wei Chang, Darsen D. Lu
• Abstract summary: This paper presents a simulation platform, namely CIMulator, for quantifying the efficacy of various synaptic devices in neuromorphic accelerators. Non-volatile 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.
• Score: 0.5325753548715747
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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