From 3b87b8bd271ade1622149516ae14f94b7ff20dd5 Mon Sep 17 00:00:00 2001
From: Tanner Robison <Robison.tnr@gmail.com>
Date: Wed, 17 Jun 2026 18:14:20 -0700
Subject: neurobench & memtorch

---
 neurobench_testing/examples/gsc/benchmark_snn.py | 53 ++++++++++++++++++++++++
 1 file changed, 53 insertions(+)
 create mode 100644 neurobench_testing/examples/gsc/benchmark_snn.py

(limited to 'neurobench_testing/examples/gsc/benchmark_snn.py')

diff --git a/neurobench_testing/examples/gsc/benchmark_snn.py b/neurobench_testing/examples/gsc/benchmark_snn.py
new file mode 100644
index 0000000..48bea78
--- /dev/null
+++ b/neurobench_testing/examples/gsc/benchmark_snn.py
@@ -0,0 +1,53 @@
+import os
+import torch
+
+from torch.utils.data import DataLoader
+
+from neurobench.datasets import SpeechCommands
+from neurobench.processors.preprocessors import S2SPreProcessor
+from neurobench.processors.postprocessors import ChooseMaxCount
+
+from neurobench.models import SNNTorchModel
+from neurobench.benchmarks import Benchmark
+
+from neurobench.metrics.workload import (
+    ActivationSparsity,
+    SynapticOperations,
+    ClassificationAccuracy
+)
+from neurobench.metrics.static import (
+    Footprint,
+    ConnectionSparsity,
+)
+
+from SNN import net
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+file_path = os.path.dirname(os.path.abspath(__file__))
+model_path = os.path.join(file_path, "model_data/s2s_gsc_snntorch")
+data_dir = os.path.join(file_path, "../../data/speech_commands") # data in repo root dir
+
+test_set = SpeechCommands(path=data_dir, subset="testing")
+
+test_set_loader = DataLoader(test_set, batch_size=500, shuffle=True)
+
+net.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
+
+## Define model ##
+model = SNNTorchModel(net)
+
+preprocessors = [S2SPreProcessor(device=device)]
+postprocessors = [ChooseMaxCount()]
+
+static_metrics = [Footprint, ConnectionSparsity]
+workload_metrics = [ClassificationAccuracy, ActivationSparsity, SynapticOperations]
+
+benchmark = Benchmark(model, test_set_loader, preprocessors, postprocessors, [static_metrics, workload_metrics])
+results = benchmark.run(device=device)
+print(results)
+
+# Results:
+# {'Footprint': 583900, 'ConnectionSparsity': 0.0, 
+# 'ClassificationAccuracy': 0.85633802969095, 'ActivationSparsity': 0.9668664144456199, 
+# 'SynapticOperations': {'Effective_MACs': 0.0, 'Effective_ACs': 3289834.3206724217, 'Dense': 29030400.0}}
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