[minibench] Drop outliers from benchmark result (pytorch#8919)

Currently the result has large variance from outliers, so only use
80% samples in the middle (trimmean 0.2)

Author: kirklandsign

extension/benchmark/android/benchmark/app/src/main/java/org/pytorch/minibench/BenchmarkActivity.java

@@ -20,6 +20,7 @@
 import java.io.IOException;
 import java.util.ArrayList;
 import java.util.Arrays;
+import java.util.Collections;
 import java.util.List;
 import java.util.stream.Collectors;
 import org.pytorch.executorch.Module;
@@ -80,11 +81,18 @@ protected void onPostExecute(Void aVoid) {
         final List<BenchmarkMetric> results = new ArrayList<>();
         // The list of metrics we have atm includes:
         // Avg inference latency after N iterations
+        // Currently the result has large variance from outliers, so only use
+        // 80% samples in the middle (trimmean 0.2)
+        Collections.sort(stats.latency);
+        int resultSize = stats.latency.size();
+        List<Double> usedLatencyResults =
+            stats.latency.subList(resultSize / 10, resultSize * 9 / 10);
+
         results.add(
             new BenchmarkMetric(
                 benchmarkModel,
                 "avg_inference_latency(ms)",
-                stats.latency.stream().mapToDouble(l -> l).average().orElse(0.0f),
+                usedLatencyResults.stream().mapToDouble(l -> l).average().orElse(0.0f),
                 0.0f));
         // Model load time
         results.add(