Wednesday 05 March 2025
Deep learning has revolutionized many fields, from self-driving cars to medical imaging, but its deployment can be a daunting task. Not only do neural networks require massive amounts of data and computational power, but they also need to be optimized for specific hardware platforms, like FPGAs or CPUs.
To alleviate this problem, researchers have been exploring ways to streamline the process of designing and deploying deep learning models. One promising approach is called neural architecture codesign (NAC), which aims to automate the discovery of optimal network architectures that can run efficiently on a variety of devices.
In a recent paper, a team of scientists from the University of California San Diego and Fermi National Accelerator Laboratory has made significant strides in this area. They’ve developed a novel pipeline that combines neural architecture search (NAS) with model compression techniques to find hardware-efficient models for fast physics applications.
The researchers’ approach is based on a two-stage process. First, they employ NAS to explore a vast parameter space of potential architectures, evaluating their performance on specific tasks like Bragg peak finding in materials science or jet classification in high-energy physics. This stage is computationally expensive but allows the team to identify promising candidates that can be fine-tuned and compressed later.
In the second stage, the researchers use model compression techniques like quantization-aware training and neural network pruning to reduce the size and latency of the most promising models. These methods involve approximating floating-point numbers with lower-precision integers or removing unnecessary connections between neurons in the network.
The team’s pipeline is designed to work seamlessly with high-level synthesis (HLS) tools, which can automatically generate FPGA code from optimized neural networks. This enables them to deploy their models on a variety of hardware platforms without requiring extensive expertise in low-level programming.
The researchers have demonstrated the effectiveness of their approach by applying it to two real-world problems: Bragg peak finding and jet classification. In both cases, they’ve achieved models with improved accuracy, smaller latencies, or reduced resource utilization relative to baseline models.
One of the key advantages of NAC is its flexibility. The pipeline can be easily extended to other tasks and domains by modifying the search space and evaluation criteria. This makes it an attractive solution for researchers who need to deploy deep learning models in a variety of settings.
The development of NAC has significant implications for many fields, from particle physics to materials science.
Cite this article: “Automating Deep Learning Model Deployment with Neural Architecture Codesign”, The Science Archive, 2025.
Neural Architecture Codesign, Deep Learning, Neural Network Compression, Model Optimization, Hardware Efficiency, Fpga, High-Level Synthesis, Particle Physics, Materials Science, Computer Vision, Machine Learning.
