From 8671410a0f9bdd4b405762ad4790c15d601b0fba Mon Sep 17 00:00:00 2001
From: Your Name <you@example.com>
Date: Fri, 28 Aug 2026 13:42:10 -0700
Subject: online crossbar inputs

---
 src/application.c | 82 ++++++++++++++++++++++++++++++++++++++-----------------
 1 file changed, 57 insertions(+), 25 deletions(-)

(limited to 'src/application.c')

diff --git a/src/application.c b/src/application.c
index b2105ce..6a868b3 100644
--- a/src/application.c
+++ b/src/application.c
@@ -5,6 +5,7 @@
 #include <spires.h>
 #include <stdio.h>
 #include <stdlib.h>
+#include <string.h>
 
 // spires reservoir parameters
 #define NUM_NEURONS 400
@@ -20,12 +21,37 @@
 #define PI 3.14159265358979323846
 #define LAMBDA 1.0e-4
 
-#define NUM_TRAINING_STEPS 50
+#define NUM_TRAINING_STEPS 500
 #define NUM_CROSSBAR_COLUMNS (NUM_OUTPUTS * 2)
 // #define NUM_STEPS 2000
 
-int main(void)
+typedef enum {
+	BENCHMARK_ONLINE,
+	BENCHMARK_OFFLINE,
+} Benchmark_Mode;
+
+static int parse_mode(int argc, char **argv, Benchmark_Mode *mode)
+{
+	if (argc == 1 || (argc == 2 && strcmp(argv[1], "--online") == 0)) {
+		*mode = BENCHMARK_ONLINE;
+		return 0;
+	}
+	if (argc == 2 && strcmp(argv[1], "--offline") == 0) {
+		*mode = BENCHMARK_OFFLINE;
+		return 0;
+	}
+	fprintf(stderr, "Usage: %s [--online|--offline]\n", argv[0]);
+	return -1;
+}
+
+int main(int argc, char **argv)
 {
+	Benchmark_Mode mode;
+	if (parse_mode(argc, argv, &mode) != 0)
+		return -1;
+	printf("Benchmark mode: %s\n",
+	       mode == BENCHMARK_ONLINE ? "online" : "offline");
+
 	/* ---------- LIST ALL MODELS HERE ----------*/
 	MemModel models[] = {
 	    {
@@ -110,25 +136,23 @@ int main(void)
 		return -1;
 	}
 
-	/* ---------- Collect reservoir states ----------*/
 	Reservoir_State_Matrix state_matrix = {0};
-	if (collect_reservoir_states(reservoir, training_inputs,
-				     NUM_TRAINING_STEPS, &state_matrix) != 0) {
-		fprintf(stderr, "Failed to collect reservoir states");
-		spires_reservoir_destroy(reservoir);
-		return -1;
-	}
-	printf("collected state matrix size: %zu x %zu\n",
-	       state_matrix.num_samples, state_matrix.num_features);
-
-	/* ---------- Generate raster plot ----------*/
-	if (plot_raster(&state_matrix, NUM_NEURONS, 0.5) != 0) {
-		fprintf(stderr, "Failed to plot raster");
+	if (mode == BENCHMARK_OFFLINE) {
+		if (collect_reservoir_states(reservoir, training_inputs,
+					     NUM_TRAINING_STEPS,
+					     &state_matrix) != 0) {
+			fprintf(stderr, "Failed to collect reservoir states");
+			spires_reservoir_destroy(reservoir);
+			return -1;
+		}
+		printf("Collected offline state matrix: %zu x %zu\n",
+		       state_matrix.num_samples, state_matrix.num_features);
+		if (plot_raster(&state_matrix, NUM_NEURONS, 0.5) != 0)
+			fprintf(stderr, "Failed to plot raster\n");
 	}
 
 	/* ---------- Run Benchmark on each model ----------*/
-	size_t predictions_per_model =
-	    state_matrix.num_samples * config.num_outputs;
+	size_t predictions_per_model = NUM_TRAINING_STEPS * config.num_outputs;
 
 	double *predictions = malloc(model_count * NUM_OUTPUTS *
 				     NUM_TRAINING_STEPS * sizeof(double));
@@ -147,10 +171,19 @@ int main(void)
 		printf("\n\nRunning benchmark on %s\n",
 		       models[model].model_path);
 
-		if (run_benchmark(&config, reservoir, &state_matrix,
-				  models[model].model_path,
-				  models[model].subcircuit_name,
-				  model_predictions) < 0) {
+		int benchmark_status;
+		if (mode == BENCHMARK_ONLINE) {
+			benchmark_status = run_online_benchmark(
+			    &config, reservoir, training_inputs,
+			    NUM_TRAINING_STEPS, models[model].model_path,
+			    models[model].subcircuit_name, model_predictions);
+		} else {
+			benchmark_status = run_benchmark(
+			    &config, reservoir, &state_matrix,
+			    models[model].model_path,
+			    models[model].subcircuit_name, model_predictions);
+		}
+		if (benchmark_status < 0) {
 			fprintf(stderr, "Failed to run benchmark");
 			free(predictions);
 			free_reservoir_state_matrix(&state_matrix);
@@ -159,12 +192,12 @@ int main(void)
 		}
 
 		plot_reservoir_predictions(
-		    target_outputs, model_predictions, state_matrix.num_samples,
+		    target_outputs, model_predictions, NUM_TRAINING_STEPS,
 		    config.num_outputs, 0, models[model].model_path);
 
 		mean_squared_error[model] =
 		    calculate_MSE(target_outputs, model_predictions,
-				  state_matrix.num_samples, config.num_outputs);
+				  NUM_TRAINING_STEPS, config.num_outputs);
 	}
 
 	double *fixed_predictions = predictions;
@@ -174,8 +207,7 @@ int main(void)
 		model_predictions = predictions + model * predictions_per_model;
 
 		if (plot_model_delta(fixed_predictions, model_predictions,
-				     state_matrix.num_samples,
-				     config.num_outputs, 0,
+				     NUM_TRAINING_STEPS, config.num_outputs, 0,
 				     models[model].model_path) < 0) {
 			fprintf(stderr, "Failed to plot model delta\n");
 		}
-- 
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