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.gitignore

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+*.py[cod]
+*$py.class
+
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README.md

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+# Scalable Event-by-event Processing of Neuromorphic Sensory Signals With Deep State-Space Models
+![Figure 1](docs/figure1.png)
+This is the official implementation of our paper [Scalable Event-by-event Processing of Neuromorphic Sensory Signals With Deep State-Space Models
+](https://arxiv.org/abs/2404.18508).
+The core motivation for this work was the irregular time-series modeling problem presented in the paper [Simplified State Space Layers for Sequence Modeling
+](https://arxiv.org/abs/2208.04933). 
+We acknowledge the awesome [S5 project](https://github.com/lindermanlab/S5) and the trainer class provided by this [UvA tutorial](https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/guide4/Research_Projects_with_JAX.html), which highly influenced our code.
+
+Our project treats a quite general machine learning problem:
+Modeling **long sequences** that are **irregularly** sampled by a possibly large number of **asynchronous** sensors.
+This problem is particularly present in the field of neuromorphic computing, where event-based sensors emit up to millions events per second from asynchronous channels.
+
+We show how linear state-space models can be tuned to effectively model asynchronous event-based sequences.
+Our contributions are
+- Integration of dirac delta coded event streams
+- time-invariant input normalization to effectively learn from long event-streams
+- formulating neuromorphic event-streams as a language modeling problem with **asynchronous tokens**
+- effectively model event-based vision **without frames and without CNNs** 
+
+## Installation
+The project is implemented in [JAX](https://github.com/google/jax) with [Flax](https://flax.readthedocs.io/en/latest/). 
+By default, we install JAX with GPU support with CUDA >= 12.0.
+To install JAX for CPU, replace `jax[cuda]` with `jax[cpu]` in the `requirements.txt` file.
+PyTorch is only required for loading data.
+Therefore, we install only the CPU version of PyTorch.
+Install the requirements with
+ ```bash
+ pip install -r requirements.txt
+ ```
+Install this repository
+ ```bash
+ pip install -e .
+ ```
+We tested with JAX versions between `0.4.20` and `0.4.29`.
+Different CUDA and JAX versions might result in slightly different results.
+
+## Reproducing experiments
+We use the [hydra](https://hydra.cc/docs/intro/) package to manage configurations.
+If you are not familiar with hydra, we recommend to read the [documentation](https://hydra.cc/docs/intro/).
+
+### Run benchmark tasks
+The basic command to run an experiment is
+```bash
+python run_training.py
+```
+This will default to running the Spiking Heidelberg Digits (SHD) dataset.
+All benchmark tasks are defined by the configurations in `configs/tasks/`, and can be run by specifying the `task` argument.
+E.g. run the Spiking Speech Commands (SSC) task with
+```bash
+python run_training.py task=spiking-speech-commands
+```
+or run the DVS128 Gestures task with
+```bash
+python run_training.py task=dvs-gesture
+```
+
+### Trained models
+We provide our best models for [download](https://datashare.tu-dresden.de/s/g2dQCi792B8DqnC).
+Check out the `tutorial_inference.ipynb` notebook to see how to load and run inference with these models.
+We also provide a script to evaluate the models on the test set
+```bash
+python run_evaluation.py task=spiking-speech-commands checkpoint=downloaded/model/SSC
+```
+
+
+### Specify HPC system and logging
+Many researchers operate on different HPC systems and perhaps log their experiments to multiple platforms.
+Therefore, the user can specify configurations for 
+- different systems (directories for reading data and saving outputs)
+- logging methods (e.g. whether to log locally or to [wandb](https://wandb.ai/))
+
+By default, the `configs/system/local.yaml` and `configs/logging/local.yaml` configurations are used, respectively.
+We suggest to create new configs for the HPC systems and wandb projects you are using.
+
+For example, to run the model on SSC with your custom wandb logging config and your custom HPC specification do
+```bash
+python run_training.py task=spiking-speech-commands logging=wandb system=hpc
+```
+where `configs/logging/wandb.yaml` should look like
+```yaml
+log_dir: ${output_dir}
+interval: 1000
+wandb: False
+summary_metric: "Performance/Validation accuracy"
+project: wandb_project_name
+entity: wandb_entity_name
+```
+and `configs/system/hpc.yaml` should specify data and output directories
+```yaml
+# @package _global_
+
+data_dir: my/fast/storage/location/data
+output_dir: my/job/output/location/${task.name}/${oc.env:SLURM_JOB_ID}/${now:%Y-%m-%d-%H-%M-%S}
+```
+The string `${task.name}/${oc.env:SLURM_JOB_ID}/${now:%Y-%m-%d-%H-%M-%S}` will create subdirectories named by task, slurm job ID, and date,
+which we found useful in practice.
+This specification of the `output_dir` is not required though.
+
+## Tutorials
+To get started with event-based state-space models, we created tutorials for training and inference.
+- `tutorial_training.ipynb` shows how to train a model on a reduced version of the Spiking Heidelberg Digits with just two classes. The model converges after few minutes on CPUs.
+- `tutorial_inference.ipynb` shows how to load a trained model and run inference. The models are available for download from the provided [download link](https://datashare.tu-dresden.de/s/g2dQCi792B8DqnC).
+- `tutorial_online_inference.ipynb` runs event-by-event inference with batch size one (online inference) on the DVS128 Gestures dataset and measures the throughput of the model.
+
+## Help and support
+We are eager to help you with any questions or issues you might have. 
+Please use the GitHub issue tracker for questions and to report issues.
+
+## Citation
+Please use the following when citing our work:
+```
+@misc{Schoene2024,
+      title={Scalable Event-by-event Processing of Neuromorphic Sensory Signals With Deep State-Space Models}, 
+      author={Mark Schöne and Neeraj Mohan Sushma and Jingyue Zhuge and Christian Mayr and Anand Subramoney and David Kappel},
+      year={2024},
+      eprint={2404.18508},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}
+```

configs/base.yaml

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+defaults:
+  - _self_
+  - system: local
+  - task: spiking-heidelberg-digits
+  - logging: local
+
+seed: 1234
+checkpoint: null
+
+hydra:
+  run:
+    dir: ${output_dir}/hydra-outputs/${now:%Y-%m-%d-%H-%M-%S}

configs/logging/local.yaml

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+log_dir: ${output_dir}
+interval: 1000
+wandb: False
+summary_metric: "Performance/Validation accuracy"
+project: ???
+entity: ???

configs/model/dvs/small.yaml

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+# @package _global_
+
+model:
+  ssm_init:
+    C_init: lecun_normal
+    dt_min: 0.001
+    dt_max: 0.1
+    conj_sym: false
+    clip_eigs: true
+  ssm:
+    discretization: async
+    d_model: 128
+    d_ssm: 128
+    ssm_block_size: 16
+    num_stages: 2
+    num_layers_per_stage: 3
+    dropout: 0.25
+    classification_mode: timepool
+    prenorm: true
+    batchnorm: false
+    bn_momentum: 0.95
+    pooling_stride: 16
+    pooling_mode: timepool
+    state_expansion_factor: 2

configs/model/shd/medium.yaml

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+# @package _global_
+
+model:
+  ssm_init:
+    C_init: lecun_normal
+    dt_min: 0.004
+    dt_max: 0.1
+    conj_sym: false
+    clip_eigs: false
+  ssm:
+    discretization: async
+    d_model: 96
+    d_ssm: 128
+    ssm_block_size: 8
+    num_stages: 2
+    num_layers_per_stage: 3
+    dropout: 0.23
+    classification_mode: pool
+    prenorm: true
+    batchnorm: false
+    bn_momentum: 0.95
+    pooling_stride: 8
+    pooling_mode: avgpool
+    state_expansion_factor: 1

configs/model/shd/tiny.yaml

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+# @package _global_
+
+model:
+  ssm_init:
+    C_init: lecun_normal
+    dt_min: 0.004
+    dt_max: 0.1
+    conj_sym: false
+    clip_eigs: false
+  ssm:
+    discretization: async
+    d_model: 16
+    d_ssm: 16
+    ssm_block_size: 8
+    num_stages: 1
+    num_layers_per_stage: 6
+    dropout: 0.1
+    classification_mode: timepool
+    prenorm: true
+    batchnorm: false
+    bn_momentum: 0.95
+    pooling_stride: 32
+    pooling_mode: timepool
+    state_expansion_factor: 1