PyTorch and CEDR: Enabling Deployment of Machine Learning Models on Heterogeneous Computing Systems

Abstract

The PyTorch programming interface enables efficient deployment of machine learning models, leveraging the parallelism offered by GPU architectures. In this study, we present the integration of the PyTorch framework with a compiler and runtime ecosystem. Our aim is to demonstrate the ability to deploy PyTorch-based models on FPGA-based SoC platforms, without requiring users to possess prior FPGA-based design experience. The proposed PyTorch model transformation approach expands the range of hardware architectures that PyTorch developers can target, enabling them to take advantage of the energy-efficient execution provided by heterogeneous computing systems. Our experiments involve compiling and executing real-life applications on heterogeneous SoC configurations emulated on the Xilinx Zynq Ultrascale+ ZCU102 system. We showcase our ability to deploy three distinct PyTorch applications, encompassing object detection, visual geometry group (VGG), and speech classification, using the integrated compiler and runtime system without loss of model accuracy. Furthermore, we extend our analysis by evaluating dynamically arriving workload scenarios, consisting of a mix of PyTorch models and non-PyTorch-based applications. Through these experiments, we vary the hardware composition and scheduling heuristics. Our findings indicate that when PyTorch-based applications coexist with unrelated applications, our integrated scheduler fairly dispatches tasks to the FPGA platform's accelerator and CPU cores, without compromising the target throughput for each application.