using memTorch to simulate spires reservoir

focusing on trying to simulate a spires reservoir on a memristor crossbar using memTorch

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)