# EPIC: An Energy-Efficient, High-Performance GPGPU Computing Research Infrastructure

arXiv: https://arxiv.org/abs/1912.05848 · HTML: https://mlsanity.com/paper/1912.05848/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2019.

## Built on by (most cited first, 23 of 23)

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- [Teacher-Student Architecture for Mixed Supervised Lung Tumor Segmentation](https://mlsanity.com/paper/2112.11541/) (2021)
- [SIRA: Scaled-Integer Range Analysis for Optimizing FPGA Dataflow Neural Network Accelerators](https://mlsanity.com/paper/2508.21493/) (2025)
- [Compressing CNN models for resource-constrained systems by channel and layer pruning](https://mlsanity.com/paper/2509.08714/) (2025)
- [NorwAI's Large Language Models: Technical Report](https://mlsanity.com/paper/2601.03034/) (2026)

## Abstract (arXiv)

The pursuit of many research questions requires massive computational resources. State-of-the-art research in physical processes using simulations, the training of neural networks for deep learning, or the analysis of big data are all dependent on the availability of sufficient and performant computational resources. For such research, access to a high-performance computing infrastructure is indispensable. Many scientific workloads from such research domains are inherently parallel and can benefit from the data-parallel architecture of general purpose graphics processing units (GPGPUs). However, GPGPU resources are scarce at Norway's national infrastructure. EPIC is a GPGPU enabled computing research infrastructure at NTNU. It enables NTNU's researchers to perform experiments that otherwise would be impossible, as time-to-solution would simply take too long.