Dual_memory_pathways: Algorithm-hardware co-design of neuromorphic networks with dual memory pathways

Algorithm-hardware co-design of neuromorphic networks with dual memory pathways Pengfei Sun* [1], Zhe Su*[2], Jascha Achterberg [3], Giacomo Indiveri [2] , Dan F.M. Goodman [1], Danyal Akarca [1, 4] Imperial College London, ETH Zurich, University of Oxford, University of Cambridge These authors contributed equally to this work. Algorithm: Pengfei Sun Hardware: Zhe Su README Requirements Python 3 with the following packages installed: PyTorch numpy spikingjelly The software has been tested with CUDA libraries version 11.3 and Pytorch 1.12.1, and spikingjelly==0.0.0.0.14. pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 -f https://download.pytorch.org/whl/cu113/torch_stable.html pip install spikingjelly Examples The core module is "dual_memory_pathways.py", and you can call "ConvLMU2" function. output = convLMU2(input_dim, hidden_dim, out_dim, d, TT, t) input_dim: input dimension hidden_dim: hidden dimension out_dim: output dimension d: memory state TT: total simulation time step t: state buffer length Example implementations can be found inside different dataset folders. For the SHD/SSC, please download the dataset from (https://zenkelab.org/datasets/). Run example SHD implementation, cd shd/src python train_spiking.py -d 5 -t 40 d is the memory state and t is the state buffer length.