Graphcore C2 Card performance for image-based deep learning application: A Report
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Abstract (arXiv)
Recently, Graphcore has introduced an IPU Processor for accelerating machine learning applications. The architecture of the processor has been designed to achieve state of the art performance on current machine intelligence models for both training and inference. In this paper, we report on a benchmark in which we have evaluated the performance of IPU processors on deep neural networks for inference. We focus on deep vision models such as ResNeXt. We report the observed latency, throughput and energy efficiency.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Feb 2020.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 1; 2025: 0; 2026: 1.
5 papers.
- 2023M3ICRO: Machine Learning-Enabled Compact Photonic Tensor Core based on PRogrammable Multi-Operand Multimode Interferencecited by 5
- 2024TeMPO: Efficient Time-Multiplexed Dynamic Photonic Tensor Core for Edge AI with Compact Slow-Light Electro-Optic Modulatorcited by 5
- 2021Demonstrating Analog Inference on the BrainScaleS-2 Mobile Systemcited by 1
- 2026Radiant Foam Rendering on a Graph Processorcited by 1
- 2023Reducing Memory Requirements for the IPU using Butterfly Factorizationscited by 0
- 2026Radiant Foam Rendering on a Graph Processor7 Jan 2026
- 2024TeMPO: Efficient Time-Multiplexed Dynamic Photonic Tensor Core for Edge AI with Compact Slow-Light Electro-Optic Modulator12 Feb 2024
- 2023Reducing Memory Requirements for the IPU using Butterfly Factorizations16 Sep 2023
- 2023M3ICRO: Machine Learning-Enabled Compact Photonic Tensor Core based on PRogrammable Multi-Operand Multimode Interference31 May 2023
- 2021Demonstrating Analog Inference on the BrainScaleS-2 Mobile System29 Mar 2021