Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Astromorphic Self-Repair of Neuromorphic Hardware Systems
Authors: Zhuangyu Han, A N M Nafiul Islam, Abhronil Sengupta
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Performance evaluation of the proposed learning rule is demonstrated for an unsupervised learning framework on the MNIST and F-MNIST datasets. ... Table 1 reports the best accuracy recorded in the self-repair process for hardware-realistic faults in the network (stuck-at-faults and weight drift). The results have been averaged over 5 independent runs of the network. |
| Researcher Affiliation | Academia | Zhuangyu Han, A N M Nafiul Islam, Abhronil Sengupta School of Electrical Engineering and Computer Science Penn State University University Park, PA 16802 EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes algorithms and formulations using mathematical equations and text, but it does not present any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our implementation source code and trained models are available at https://github.com/ Neuro Comp Lab-psu/Astromorphic Self Repair. |
| Open Datasets | Yes | The SNNs are trained using STDP on two recognition tasks, namely the MNIST (Le Cun, Cortes, and Burges 2010) and Fashion MNIST (Xiao, Rasul, and Vollgraf 2017) datasets. |
| Dataset Splits | No | The paper mentions training on datasets but does not explicitly detail validation splits. It focuses on training and then injecting faults for self-repair evaluation. While common datasets like MNIST often have standard splits, the paper doesn't specify them for its experimental setup beyond training. |
| Hardware Specification | Yes | The algorithms were run on the hardware environment consisting of one Intel(R) Xeon(R) Silver 4210 CPU, one NVIDIA Ge Force RTX 2080 Ti GPU with 11264 MBytes graphics memory, 187 GBytes RAM and Cent OS Linux 7 operating system. |
| Software Dependencies | Yes | The proposed A-STDP (local) learning rule is implemented using the Binds NET (Hazan et al. 2018) framework an open-source SNN simulation platform based on Py Torch (https://pytorch.org/). The text mentions PyTorch which implies a version, even if not explicitly stated with a number. |
| Experiment Setup | Yes | The detailed information regarding all simulation hyperparameters is included in Table 2 and typical ablation studies for important hyperparameters of the astromorphic learning model are also included in the supplementary material (Han, Islam, and Sengupta 2022). |