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| Home > Publications database > Interfacing Neuromorphic Hardware with Machine Learning Frameworks - A Review |
| Home > Publications database > Interfacing Neuromorphic Hardware with Machine Learning Frameworks - A Review |
| Proceedings | FZJ-2023-03873 |
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2023
ACM New York, NY, USA
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Please use a persistent id in citations: doi:10.1145/3589737.3605967
Abstract: With the emergence of neuromorphic hardware as a promising low-power parallel computing platform, the need for tools that allowresearchers and engineers to efficiently interact with such hardwareis rapidly growing. Machine learning frameworks like Tensorflow,PyTorch and JAX have been instrumental for the success of machinelearning in recent years as they enable seamless interaction withtraditional machine learning accelerators such as GPUs and TPUs.In stark contrast, interfacing with neuromorphic hardware remainsdifficult since the aforementioned frameworks do not address thechallenges associated with mapping neural network models and al-gorithms to physical hardware. In this paper, we review the variousstrategies employed throughout the neuromorphic computing com-munity to tackle these challenges and categorize them according totheir methodologies and implementation effort. This classificationserves as a guideline for device engineers and software developersalike to enable them to choose the best-fit solution in regard of theirdemands and available resources. Finally, we provide a JAX-basedproof-of-concept implementation of a compilation pipeline tailoredto the needs of researchers in the early stages of device develop-ment, where parts of the computational graph can be mapped ontocustom hardware via operations exposed through a C++ or Pythoninterface. The code is available at https://github.com/PGI15/xbarax.
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