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Diffstat (limited to 'custom_benchmark/memTorch_SNN.py')
| -rw-r--r-- | custom_benchmark/memTorch_SNN.py | 28 |
1 files changed, 0 insertions, 28 deletions
diff --git a/custom_benchmark/memTorch_SNN.py b/custom_benchmark/memTorch_SNN.py deleted file mode 100644 index 67e9cb6..0000000 --- a/custom_benchmark/memTorch_SNN.py +++ /dev/null @@ -1,28 +0,0 @@ -import torch -import memtorch - -# 1. Standard memTorch setup (Static crossbar weights) -ann_layer = torch.nn.Linear(100, 10) -patched_layer = memtorch.mn.Module.patch_model(ann_layer, memristor_model) - -# 2. Your custom SNN wrapper loop -def forward_snn(input_spikes_over_time): - # input_spikes_over_time shape: (time_steps, batch_size, input_dim) - time_steps = input_spikes_over_time.shape[0] - v_mem = torch.zeros(batch_size, 10) # Hidden neuron membrane potentials - output_spikes = [] - - for t in range(time_steps): - # Pass binary spikes through memTorch's physical crossbar simulation - current_in = patched_layer(input_spikes_over_time[t]) - - # Leaky Integrate-and-Fire (LIF) logic (Written by you!) - v_mem = 0.9 * v_mem + current_in # Leak & Integrate - - # Fire threshold - spike = (v_mem >= 1.0).float() - v_mem[v_mem >= 1.0] = 0.0 # Reset - - output_spikes.append(spike) - - return torch.stack(output_spikes) |