Add mlperf_tiny benchmark targets

Change-Id: I38505ccc64da6168804ac56d8582c9791069f53a

WORKSPACE

diff --git a/WORKSPACE b/WORKSPACE
index 3d18b46..3e4b258 100644
--- a/WORKSPACE
+++ b/WORKSPACE
@@ -96,3 +96,11 @@
 load("@tflm_pip_deps//:requirements.bzl", install_tflm_pip_deps = "install_deps")
 
 install_tflm_pip_deps()
+
+http_archive(
+    name = "mlperf_tiny",
+    strip_prefix = "tiny-debd5310ffbad653932e2995816e853197116dd9",
+    urls = ["https://github.com/mlcommons/tiny/archive/debd5310ffbad653932e2995816e853197116dd9.zip"],
+    sha256 = "e74de33f20aabfa587d0fa35a0cbe9a1bda6a9d82475b3947bcc588142f49414",
+    build_file = "//third_party/mlperf_tiny:BUILD.mlperf_tiny",
+)

benchmarks/BUILD

diff --git a/benchmarks/BUILD b/benchmarks/BUILD
index 56d1e8e..96ad4b8 100644
--- a/benchmarks/BUILD
+++ b/benchmarks/BUILD
@@ -93,3 +93,51 @@
     model = "//quant_models:mobilenet_v2_0.50_224_int8_dummy.tflite",
     iterations = 3,
 )
+
+kelvin_benchmark_simulator(
+    name = "mlperf_tiny_image_classification_benchmark_simulator",
+    model = "@mlperf_tiny//:benchmark/training/image_classification/trained_models/pretrainedResnet_quant.tflite",
+    iterations = 1,
+)
+
+kelvin_benchmark_devices(
+    name = "mlperf_tiny_image_classification_benchmark_device",
+    model = "@mlperf_tiny//:benchmark/training/image_classification/trained_models/pretrainedResnet_quant.tflite",
+    iterations = 1,
+)
+
+kelvin_benchmark_simulator(
+    name = "mlperf_tiny_anomaly_detection_benchmark_simulator",
+    model = "@mlperf_tiny//:benchmark/training/anomaly_detection/trained_models/ad01_int8.tflite",
+    iterations = 1,
+)
+
+kelvin_benchmark_devices(
+    name = "mlperf_tiny_anomaly_detection_benchmark_device",
+    model = "@mlperf_tiny//:benchmark/training/anomaly_detection/trained_models/ad01_int8.tflite",
+    iterations = 1,
+)
+
+kelvin_benchmark_simulator(
+    name = "mlperf_tiny_visual_wake_words_benchmark_simulator",
+    model = "@mlperf_tiny//:benchmark/training/visual_wake_words/trained_models/vww_96_int8.tflite",
+    iterations = 1,
+)
+
+kelvin_benchmark_devices(
+    name = "mlperf_tiny_visual_wake_words_benchmark_device",
+    model = "@mlperf_tiny//:benchmark/training/visual_wake_words/trained_models/vww_96_int8.tflite",
+    iterations = 1,
+)
+
+kelvin_benchmark_simulator(
+    name = "mlperf_tiny_keyword_spotting_benchmark_simulator",
+    model = "@mlperf_tiny//:benchmark/training/keyword_spotting/trained_models/kws_ref_model.tflite",
+    iterations = 1,
+)
+
+kelvin_benchmark_devices(
+    name = "mlperf_tiny_keyword_spotting_benchmark_device",
+    model = "@mlperf_tiny//:benchmark/training/keyword_spotting/trained_models/kws_ref_model.tflite",
+    iterations = 1,
+)