TorchAxf: Enabling Rapid Simulation of Approximate DNN Models Using GPU-Based Floating-Point Computing Framework

This paper presents an approximate floating-point computing framework TorctiAxf1 that enables fast simulation of various approximate deep neural network (DNN) models, including spiking neural networks (SNNs), using various types of approximate adders and multipliers. Additionally, it supports the standard reduced precision floating-point formats, such as bfloat16, and any user-customized precision representation. TorchAxf leverages GPU acceleration to expedite approximate DNN training and inference running on the PyTorch framework. Any arbitrary approximate arithmetic algorithm with C/C++ behavioral models can be readily integrated with TorchAxf to emulate approximate DNN accelerators. Through extensive experiments, we reveal an appropriate degree of the floating-point arithmetic that can be approximated for DNN models without any significant accuracy loss. We also show that approximate-aware re-training can recover errors and refine pre-trained DNN models under reduced precision formats. Besides, TorchAxf running on GPU enables the simulation time of complex DNN models using approximate arithmetic to reduce up to 43.17× compared to the baseline optimized CPU implementation.