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Exploration of TPUs for AI Applications

arXiv 2309.08918 ↗First posted 16 Sep 2023cs.AR cs.AIcited by 0

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Abstract (arXiv)

Tensor Processing Units (TPUs) are specialized hardware accelerators for deep learning developed by Google. This paper aims to explore TPUs in cloud and edge computing focusing on its applications in AI. We provide an overview of TPUs, their general architecture, specifically their design in relation to neural networks, compilation techniques and supporting frameworks. Furthermore, we provide a comparative analysis of Cloud and Edge TPU performance against other counterpart chip architectures. Our results show that TPUs can provide significant performance improvements in both cloud and edge computing. Additionally, this paper underscores the imperative need for further research in optimization techniques for efficient deployment of AI architectures on the Edge TPU and benchmarking standards for a more robust comparative analysis in edge computing scenarios. The primary motivation behind this push for research is that efficient AI acceleration, facilitated by TPUs, can lead to substantial savings in terms of time, money, and environmental resources.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 50 references that are in the map. First posted on arXiv 16 Sep 2023.

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21 of 50 references.

References · 21

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  4. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  5. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  6. 2023A Survey of Large Language Modelscited by 2,576
  7. 2021GLaM: Efficient Scaling of Language Models with Mixture-of-Expertscited by 897
  8. 2017Efficient Processing of Deep Neural Networks: A Tutorial and Surveycited by 434
  9. 2017A Survey of Model Compression and Acceleration for Deep Neural Networkscited by 301
  10. 2019MLPerf Inference Benchmarkcited by 199
  11. 2020Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluationcited by 188
  12. 2022Generative Adversarial Super-Resolution at the Edge with Knowledge Distillationcited by 9
9 more
  1. 2019Scientific Image Restoration Anywherecited by 6
  2. 2023Facial Expression Recognition at the Edge: CPU vs GPU vs VPU vs TPUcited by 6
  3. 2021GPTPU: Accelerating Applications using Edge Tensor Processing Unitscited by 5
  4. 2022Deep Learning for Real Time Satellite Pose Estimation on Low Power Edge TPUcited by 4
  5. 2022A Study on the Use of Edge TPUs for Eye Fundus Image Segmentationcited by 3
  6. 2022TPU-MLIR: A Compiler For TPU Using MLIRcited by 3
  7. 2023Edge Devices Inference Performance Comparisoncited by 3
  8. 2019EdgeDRNN: Enabling Low-latency Recurrent Neural Network Edge Inferencecited by 2
  9. 2022Benchmarking GPU and TPU Performance with Graph Neural Networkscited by 1

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