# A Competitive Edge: Can FPGAs Beat GPUs at DCNN Inference Acceleration in Resource-Limited Edge Computing Applications?

arXiv: https://arxiv.org/abs/2102.00294 · HTML: https://mlsanity.com/paper/2102.00294/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Early exit and edge inference, one of 12 directions in Efficient LLM inference and serving.
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 30 Jan 2021.

## Placed in

- Efficient LLM inference and serving / [Early exit and edge inference](https://mlsanity.com/inference/early-exit-and-edge-inference/)

## Builds on (most cited first, 11 of 11)

- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [Pros and Cons of GAN Evaluation Measures](https://mlsanity.com/paper/1802.03446/) (2018)
- [A guide to convolution arithmetic for deep learning](https://mlsanity.com/paper/1603.07285/) (2016)
- [GANAX: A Unified MIMD-SIMD Acceleration for Generative Adversarial Networks](https://mlsanity.com/paper/1806.01107/) (2018)
- [Are FPGAs Suitable for Edge Computing?](https://mlsanity.com/paper/1804.06404/) (2018)
- [A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA](https://mlsanity.com/paper/1705.02583/) (2017)
- [Towards Design Methodology of Efficient Fast Algorithms for Accelerating Generative Adversarial Networks on FPGAs](https://mlsanity.com/paper/1911.06918/) (2019)
- [Towards a Uniform Architecture for the Efficient Implementation of 2D and 3D Deconvolutional Neural Networks on FPGAs](https://mlsanity.com/paper/1903.02550/) (2019)
- [hlslib: Software Engineering for Hardware Design](https://mlsanity.com/paper/1910.04436/) (2019)

## Built on by (most cited first, 6 of 6)

- [A2Q: Accumulator-Aware Quantization with Guaranteed Overflow Avoidance](https://mlsanity.com/paper/2308.13504/) (2023)
- [A2Q+: Improving Accumulator-Aware Weight Quantization](https://mlsanity.com/paper/2401.10432/) (2024)
- [An Energy-Efficient Edge Computing Paradigm for Convolution-based Image Upsampling](https://mlsanity.com/paper/2107.07647/) (2021)
- [An unsupervised approach towards promptable defect segmentation in laser-based additive manufacturing by Segment Anything](https://mlsanity.com/paper/2312.04063/) (2023)
- [Frontiers of Deep Learning: From Novel Application to Real-World Deployment](https://mlsanity.com/paper/2407.14386/) (2024)
- [Hardware-Efficient Deconvolution-Based GAN for Edge Computing](https://mlsanity.com/paper/2201.06878/) (2022)

## Abstract (arXiv)

When trained as generative models, Deep Learning algorithms have shown exceptional performance on tasks involving high dimensional data such as image denoising and super-resolution. In an increasingly connected world dominated by mobile and edge devices, there is surging demand for these algorithms to run locally on embedded platforms. FPGAs, by virtue of their reprogrammability and low-power characteristics, are ideal candidates for these edge computing applications. As such, we design a spatio-temporally parallelized hardware architecture capable of accelerating a deconvolution algorithm optimized for power-efficient inference on a resource-limited FPGA. We propose this FPGA-based accelerator to be used for Deconvolutional Neural Network (DCNN) inference in low-power edge computing applications. To this end, we develop methods that systematically exploit micro-architectural innovations, design space exploration, and statistical analysis. Using a Xilinx PYNQ-Z2 FPGA, we leverage our architecture to accelerate inference for two DCNNs trained on the MNIST and CelebA datasets using the Wasserstein GAN framework. On these networks, our FPGA design achieves a higher throughput to power ratio with lower run-to-run variation when compared to the NVIDIA Jetson TX1 edge computing GPU.