# Graphcore C2 Card performance for image-based deep learning application: A Report

arXiv: https://arxiv.org/abs/2002.11670 · HTML: https://mlsanity.com/paper/2002.11670/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2020.

## Built on by (most cited first, 5 of 5)

- [TeMPO: Efficient Time-Multiplexed Dynamic Photonic Tensor Core for Edge AI with Compact Slow-Light Electro-Optic Modulator](https://mlsanity.com/paper/2402.07393/) (2024)
- [M3ICRO: Machine Learning-Enabled Compact Photonic Tensor Core based on PRogrammable Multi-Operand Multimode Interference](https://mlsanity.com/paper/2305.19505/) (2023)
- [Demonstrating Analog Inference on the BrainScaleS-2 Mobile System](https://mlsanity.com/paper/2103.15960/) (2021)
- [Radiant Foam Rendering on a Graph Processor](https://mlsanity.com/paper/2601.04382/) (2026)
- [Reducing Memory Requirements for the IPU using Butterfly Factorizations](https://mlsanity.com/paper/2309.08946/) (2023)

## 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.