deep neural network (DNN)

Deep Neural Networks (DNNs) are a class of artificial neural networks that have multiple layers between the input and output layers, which enable the modeling of complex data with high levels of abstraction. DNNs are widely used in various applications, including image and speech recognition, natural language processing, and autonomous vehicles.

GPU Acceleration for DNNs

GPUs are particularly well-suited for DNN training and inference due to their ability to perform a large number of floating-point operations in parallel. This parallelism is beneficial for the matrix and vector operations that are common in DNN computations. The use of GPUs can significantly reduce the time required to train and run DNNs compared to using CPUs alone[1][3][4].

GPGPU vs. TPU for DNNs

While General-Purpose GPUs (GPGPUs) have been instrumental in the advances in machine learning, they have some disadvantages when compared to Tensor Processing Units (TPUs) for DNN workloads. GPGPUs incur high overhead in performance, area, and energy due to heavy multithreading, which is unnecessary for DNNs that have prefetchable, sequential memory accesses. TPUs, on the other hand, are designed specifically for tensor operations and can capture DNNs' data reuse more efficiently[2][5].

Challenges and Solutions

Programming for GPGPUs can be challenging due to the complexity of their architecture and the need for specialized knowledge in parallel programming. However, new tools and languages, such as Triton, are emerging to make GPU programming more accessible to researchers without extensive CUDA experience[6].

Benchmarking and Performance

Benchmarking studies have been conducted to compare the performance of GPUs and TPUs on various neural network models, including Graph Neural Networks (GNNs). These studies help in understanding the strengths and weaknesses of different hardware architectures for specific types of neural network models[7].

Conclusion

In summary, GPUs and GPGPUs play a crucial role in accelerating DNN training and inference, offering significant speedups over traditional CPU-based computations. However, for certain DNN workloads, TPUs may offer better performance due to their specialized architecture. As the field of AI and machine learning continues to evolve, we can expect further advancements in both hardware and software to optimize DNN performance across various platforms.

Citations:

[1] https://ieeexplore.ieee.org/document/9582157

[2] https://www.sigarch.org/why-the-gpgpu-is-less-efficient-than-the-tpu-for-dnns/

[3] https://arxiv.org/abs/2203.05705

[4] https://lambdalabs.com/gpu-benchmarks

[5] https://premioinc.com/blogs/blog/what-is-the-difference-between-cpu-vs-gpu-vs-tpu-complete-overview

[6] https://openai.com/research/triton

[7] https://arxiv.org/abs/2210.12247

[8] https://www.linkedin.com/pulse/cpu-vs-gpu-tpu-shri-jayan-rajendran?trk=article-ssr-frontend-pulse_more-articles_related-content-card

[9] https://www.sciencedirect.com/science/article/abs/pii/S2214579616300405

[10] http://mvapich.cse.ohio-state.edu/static/media/talks/slide/awan-mlhpc17.pdf

[11] https://towardsdatascience.com/when-to-use-cpus-vs-gpus-vs-tpus-in-a-kaggle-competition-9af708a8c3eb

[12] https://pyimagesearch.com/2020/02/10/opencv-dnn-with-nvidia-gpus-1549-faster-yolo-ssd-and-mask-r-cnn/

[13] https://deci.ai/blog/close-gap-cpu-performance-gpu-deep-learning-models/

[14] https://windowsreport.com/tpu-vs-gpu/

[15] https://utd-ir.tdl.org/server/api/core/bitstreams/c943bd2c-3956-48c9-9541-9c326c116485/content

[16] https://www.usenix.org/conference/nsdi23/presentation/you

[17] https://ar5iv.labs.arxiv.org/html/1907.10701

[18] https://dl.acm.org/doi/fullHtml/10.1145/3613424.3614277

[19] https://dl.acm.org/doi/10.1145/3613424.3614277

[20] https://timdettmers.com/2023/01/30/which-gpu-for-deep-learning/

[21] https://subscription.packtpub.com/book/game-development/9781788993913/9

[22] https://docs.nvidia.com/deeplearning/performance/dl-performance-gpu-background/index.html