Name
Title | ||
|---|---|---|
FlexiGAN: An End-to-End Solution for FPGA Acceleration of Generative Adversarial Networks |
Abstract | ||
|---|---|---|
Generative Adversarial Networks (GANs) are a frontier in deep learning. GANs consist of two models: generative and discriminative. While the discriminative model uses the conventional convolution, the generative model depends on a fundamentally different operator, called transposed convolution. This operator initially inserts a large number of zeros in its input and then slides a window over this expanded input. This zero-insertion step leads to a large number of ineffectual operations and creates distinct patterns of computation across the sliding windows. The ineffectual operations along with the variation in computation patterns lead to significant resource underutilization when using conventional convolution hardware. To alleviate these sources of inefficiency, this paper devises FlexiGAN, an end-to-end solution, that generates an optimized synthesizable FPGA accelerator from a high-level GAN specification. FlexiGAN is coupled with a novel template architecture that aims to harness the benefits of both MIMD and SIMD execution models to avoid ineffectual operations. To this end, the proposed architecture separates data retrieval and data processing units at the finest granularity of each compute engine. Leveraging this separation enables the architecture to use a succinct set of operations to cope with the irregularities of transposed convolution. At the same time, it significantly reduces the on-chip memory usage, which is generally limited in FPGAs. We evaluate our end-to-end solution by generating FPGA accelerators for a variety of GANs. These generated accelerators provide 2.4× higher performance than an optimized conventional convolution design. In addition, FlexiGAN, on average, yields 2.8× (up to 3.7×) improvements in Performance-per-Watt over a Titan X GPU. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/FCCM.2018.00019 | 2018 IEEE 26th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM) |
Keywords | Field | DocType |
Generative Adversarial Network,MIMD SIMD,Accelerator,machine learning | Data processing,Data retrieval,Convolution,Computer science,Parallel computing,SIMD,Discriminative model,Generative model,MIMD,Computation | Conference |
ISBN | Citations | PageRank |
978-1-5386-5523-8 | 6 | 0.47 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amir Yazdanbakhsh | 1 | 241 | 15.28 |
| Michael Brzozowski | 2 | 6 | 0.47 |
| Behnam Khaleghi | 3 | 91 | 13.49 |
| Soroush Ghodrati | 4 | 13 | 1.94 |
| Kambiz Samadi | 5 | 817 | 43.11 |
| Nam Sung Kim | 6 | 3268 | 225.99 |
| H. Esmaeilzadeh | 7 | 1443 | 69.71 |