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"""
This is a program to test out using memTorch with neurobench.
June 17th, 2026
Author: Tanner Robison,
Teuscher Lab
"""
import torch
import torch.nn as nn
import snntorch as snn
from snntorch import surrogate
from torch.utils.data import DataLoader
from neurobench.models import SNNTorchModel
from neurobench.benchmarks import Benchmark, benchmark
from neurobench.datasets import SpeechCommands
from neurobench.metrics.workload import (
ActivationSparsity,
SynapticOperations,
ClassificationAccuracy,
)
from neurobench.metrics.static import (
Footprint,
ConnectionSparsity,
)
from neurobench.processors.preprocessors import S2SPreProcessor
from neurobench.processors.postprocessors import ChooseMaxCount
import copy
from memtorch.mn.Module import patch_model
from memtorch.map.Parameter import naive_map
from memtorch.bh.memristor import VTEAM
beta = 0.9
class SNN(nn.Module):
def __init__(self):
super().__init__()
#standard layers
self.fc1 = nn.Linear(20, 128)
self.fc2 = nn.Linear(128, 35)
#Spiking neurons
self.lif1 = snn.Leaky(beta=beta, init_hidden=True)
self.lif2 = snn.Leaky(beta=beta, init_hidden=True, output=True)
def forward(self, x):
x = x.view(x.size(0), -1)
cur1 = self.fc1(x)
spk1 = self.lif1(cur1)
cur2 = self.fc2(spk1)
spk2, mem2 = self.lif2(cur2)
return spk2, mem2
device = torch.device("cpu")
spike_grad = surrogate.fast_sigmoid()
net = nn.Sequential(
nn.Flatten(),
nn.Linear(20, 256),
snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True),
nn.Linear(256, 256),
snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True),
nn.Linear(256, 256),
snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True),
nn.Linear(256, 35),
snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True, output=True),
)
#memristor patch
reference_memristor = VTEAM()
net.load_state_dict(torch.load("examples/gsc/model_data/s2s_gsc_snntorch", map_location=device))
patched_net = patch_model(
copy.deepcopy(net),
memristor_model=reference_memristor,
memristor_model_params={'time_series_resolution': 1e-8},
mapping_routine=naive_map,
transistor=True,
tile_shape=(128, 128),
ADC_resolution=8,
use_bindings=True
)
static_metrics = [Footprint, ConnectionSparsity]
workload_metrics = [ActivationSparsity, SynapticOperations, ClassificationAccuracy]
# data loader here maybe??
test_set = SpeechCommands(path="data/SpeechCommands/", subset="testing")
test_set_loader = DataLoader(test_set, batch_size=500, shuffle=True)
pre_processor = [S2SPreProcessor(device=device)]
post_processor = [ChooseMaxCount()]
model = SNNTorchModel(net)
benchmark = Benchmark(
model,
test_set_loader,
pre_processor,
post_processor,
[static_metrics, workload_metrics]
)
results = benchmark.run()
print("\n\n----- IDEAL BENCHMARK -----")
print(f"Footprint: {results['Footprint']}")
print(f"Connection Sparsity: {results['ConnectionSparsity']}")
print(f"Activation Sparsity: {results['ActivationSparsity']}")
print(f"Synaptic Operations: {results['SynapticOperations']}")
print(f"Classification Accuracy: {results['ClassificationAccuracy']}")
#energy calculations
ENERGY_PER_MAC = 0.9e-12
ENERGY_PER_AC = 0.1e-12
macs = results['SynapticOperations']['Effective_MACs']
acs = results['SynapticOperations']['Effective_ACs']
total_energy = (macs * ENERGY_PER_MAC) + (acs * ENERGY_PER_AC)
print("Energy Report:")
print(f"Total Operations: {macs} MACS, {acs} acs ")
print(f"Calculated Energy cost: {total_energy} joules per batch")
model = SNNTorchModel(patched_net)
benchmark = Benchmark(
model,
test_set_loader,
pre_processor,
post_processor,
[static_metrics, workload_metrics]
)
with torch.no_grad(): #makes sure its in inference mode
#otherwise you get memory leaks
results = benchmark.run()
print("\n\n----- MEMRISTOR BENCHMARK -----")
print(f"Footprint: {results['Footprint']}")
print(f"Connection Sparsity: {results['ConnectionSparsity']}")
print(f"Activation Sparsity: {results['ActivationSparsity']}")
print(f"Synaptic Operations: {results['SynapticOperations']}")
print(f"Classification Accuracy: {results['ClassificationAccuracy']}")
#energy calculations
ENERGY_PER_MAC = 0.9e-12
ENERGY_PER_AC = 0.1e-12
macs = results['SynapticOperations']['Effective_MACs']
acs = results['SynapticOperations']['Effective_ACs']
total_energy = (macs * ENERGY_PER_MAC) + (acs * ENERGY_PER_AC)
print("Energy Report:")
print(f"Total Operations: {macs} MACS, {acs} acs ")
print(f"Calculated Energy cost: {total_energy} joules per batch")
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