1. CIMulator: A Comprehensive Simulation Platform for Computing-In-Memory Circuit Macros with Low Bit-Width and Real Memory Materials
- Author
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Le, Hoang-Hiep, Baig, Md. Aftab, Hong, Wei-Chen, Tsai, Cheng-Hsien, Yeh, Cheng-Jui, Liang, Fu-Xiang, Huang, I-Ting, Tsai, Wei-Tzu, Cheng, Ting-Yin, De, Sourav, Chen, Nan-Yow, Lee, Wen-Jay, Lin, Ing-Chao, Chang, Da-Wei, and Lu, Darsen D.
- Subjects
Computer Science - Neural and Evolutionary Computing - 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.
- Published
- 2023