diff --git a/hpvm/test/README.rst b/hpvm/test/README.rst
index 66af5c999fd4c90df193f6ca22cc911419d66f40..6125aab2b78e8a94e4544cbf142273e623723a41 100644
--- a/hpvm/test/README.rst
+++ b/hpvm/test/README.rst
@@ -4,20 +4,17 @@ Test and Benchmarks
 Directory Organization
 ----------------------
 
-The `hpvm/test` directory holds all tests and benchmarks in HPVM and is organized as follows:
+The ``hpvm/test`` directory holds all tests and benchmarks in HPVM and is organized as follows:
 
-* 
-  ``hpvm_pass/``: unit and regression tests for HPVM Passes, written in LLVM-bitcode.
+* ``hpvm_pass/``: unit and regression tests for HPVM Passes, written in LLVM-bitcode.
 
-* 
-  ``benchmarks/``: includes a few applications written in HPVM-C, a template, and directions for compiling and running these benchmarks.
+* ``benchmarks/``: includes a few applications written in HPVM-C, a template, and directions for compiling and running these benchmarks.
 
   * ``benchmarks/parboil``: Selected benchmarks from the `Parboil <http://impact.crhc.illinois.edu/parboil/parboil.aspx>`_ benchmark suite.
   * ``benchmarks/pipeline``: An edge detection pipeline benchmark.
   * ``benchmarks/hpvm-cava``: A Camera ISP pipeline, adapted from C code provided from our collaborators at `Harvard <http://vlsiarch.eecs.harvard.edu>`_.
 
-* 
-  ``dnn_benchmarks/``: ten (10) DNN benchmarks in HPVM-C, Keras and PyTorch, supported by ApproxHPVM.
+* ``dnn_benchmarks/``: ten (10) DNN benchmarks in HPVM-C, Keras and PyTorch, supported by ApproxHPVM.
   This tests HPVM as well as the Keras and PyTorch frontends.
 
   * 
@@ -25,18 +22,27 @@ The `hpvm/test` directory holds all tests and benchmarks in HPVM and is organize
     Their organization and usage are similar to the benchmarks under ``benchmarks/``.
 
     Each subfolder contains a DNN with 2 versions (2 ``.cpp`` files):
-    the ``tensor``-targeted version which compiles to ``tensor_runtime``,
+    the ``tensor``-targeted version which compiles to `tensor_runtime`,
     and the ``cudnn``-targeted version which compiles to operators in ``cuDNN``
     (has ``_cudnn`` in name).
 
-  * 
-    ``dnn_benchmarks/keras`` contains these DNNs implemented in Keras,
+  * ``dnn_benchmarks/keras`` contains these DNNs implemented in Keras,
     and code for generating them down to HPVM-C (testing Keras frontend).
 
   * ``dnn_benchmarks/pytorch`` contains these DNNs in PyTorch
     and code for generating them down to HPVM-C (testing PyTorch/ONNX frontend).
 
-  The code generated from Keras and PyTorch frontend should be largely similar and functionally equivalent.
+    * ``./dnn`` is a local package with these 10 DNNs implemented in PyTorch as examples.
+      This package is not installed with HPVM.
+
+    * ``./test_frontend`` contains tests on inference accuracy of code generated by the PyTorch frontend.
+
+    * ``./test_{profiling|tuning}`` contains tests on performing profiling/tuning
+      on frontend-generated binary.
+
+  * ``dnn_benchmarks/tensor-rt-src`` contains these DNNs directly implemented in `tensor_runtime`
+    functions. These are for reference purpose only and not actively used in the HPVM system or testing.
+
 
 Running Test Cases and Benchmarks
 ---------------------------------
@@ -45,29 +51,29 @@ The easiest way to run tests is to use ``make`` targets,
 which will also take care of all compilation of test cases and test fixtures.
 The following targets runs these tests respectively:
 
-
 * ``make -j check-hpvm-pass`` runs tests in ``hpvm_pass``: ``hpvm_pass/**/*.ll``.
   These are regression and unit tests for HPVM passes.
-* 
-  ``make -j check-hpvm-dnn`` runs all 20 DNN benchmarks under ``dnn_benchmarks/hpvm-c``
+
+* ``make -j check-hpvm-dnn`` runs all 20 DNN benchmarks under ``dnn_benchmarks/hpvm-c``
   (10 DNNs x 2 versions) and validates their accuracy.
 
   *Note* that this can take quite long due to the size of DNNs and datasets.
   Depending on your hardware capability, this test can take 5-30 minutes.
   Also, this is set to run sequentially out of GPU memory concerns.
 
-* 
-  ``make -j check-hpvm-profiler`` runs ``hpvm-profiler`` on some smaller networks
-  (as it is extremely time-consuming) and presents the tradeoff curve with profiled speedup.
+* ``make -j check-hpvm-torch-acc`` generates all 10 DNNs with torch frontend,
+  runs them and checks their accuracy. This tests the torch frontend in isolation.
 
-  *Note* that if you're on an NVIDIA Jetson TX2, you may want to run
-  ``bash dnn_benchmarks/profiling/jetson_clocks.sh``
-  to ensure that the clocks are running at the maximum frequency
+* ``make -j check-hpvm-torch-tuning`` runs `predtuner` with binaries from torch frontend
+  to exercise both empirical and predictive autotuning.
+  This is only done for a few smaller networks for 5 iterations,
+  as it is extremely time-consuming.
 
-Underneath, ``llvm-lit`` is used to discover and run the tests.
+* ``make -j check-hpvm-torch-profiling`` runs `hpvm-profiler` with binaries from torch frontend,
+  and presents the tradeoff curve with profiled speedup.
+  This is only done for a few smaller networks.
 
-``benchmarks/`` can only be compiled in-source with ``make``.
-We are working to migrate it into the ``cmake`` system.
+Underneath, ``llvm-lit`` is used to discover and run the tests.
 
 Compiling Benchmarks
 --------------------
@@ -119,4 +125,20 @@ Currently, there are 20 of them. These are:
 
 
 ``_cudnn`` suffix indicates the code is generated onto cuDNN functions.
-Otherwise they are generated to ``tensor_runtime`` DNN functions which are hand-written in CUDA.
+Otherwise they are generated to `tensor_runtime` DNN functions which are hand-written in CUDA.
+
+Other HPVM-C Benchmarks
+^^^^^^^^^^^^^^^^^^^^^^^
+
+There are 6 benchmarks under ``benchmarks/``:
+``hpvm-cava`` and ``pipeline`` are single benchmarks, while ``parboil/`` is a collection of 4 benchmarks.
+
+To build ``hpvm-cava`` or ``pipeline``,
+use ``make -j hpvm_cava_{cpu|gpu}`` or ``make -j pipeline_{cpu|gpu}``.
+The cpu or gpu suffix indicates the device the kernels in the benchmark run on.
+For ``hpvm-cava``, the binary is generated under
+``${build_dir}/tools/hpvm/test/benchmarks/hpvm-cava``,
+while pipeline binaries are under ``${build_dir}/tools/hpvm/test/benchmarks/pipeline``.
+
+The parboil benchmarks are only available through Makefile.
+We will move them into CMake in the next release.
diff --git a/hpvm/test/dnn_benchmarks/pytorch/CMakeLists.txt b/hpvm/test/dnn_benchmarks/pytorch/CMakeLists.txt
index 09ceddfb46afc6e2d3d769cc57af04b37a929e23..9129cab70115cabc14426ccb47ee0531816592b3 100644
--- a/hpvm/test/dnn_benchmarks/pytorch/CMakeLists.txt
+++ b/hpvm/test/dnn_benchmarks/pytorch/CMakeLists.txt
@@ -5,7 +5,7 @@ configure_lit_site_cfg(
   MAIN_CONFIG
   ${CMAKE_CURRENT_SOURCE_DIR}/test_frontend/lit.cfg.py
 )
-add_lit_testsuite(check-hpvm-torch2hpvm "Run accuracy tests for HPVM PyTorch frontend"
+add_lit_testsuite(check-hpvm-torch-acc "Run accuracy tests for HPVM PyTorch frontend"
   ${CMAKE_CURRENT_BINARY_DIR}/test_frontend
   # We depend on check_dnn_acc.py defined in ../hpvm-c/
   # to compare the inference accuracy of our frontend-generated binary
@@ -21,7 +21,7 @@ configure_lit_site_cfg(
   MAIN_CONFIG
   ${CMAKE_CURRENT_SOURCE_DIR}/test_profiling/lit.cfg.py
 )
-add_lit_testsuite(check-hpvm-profiling "Run tests for frontend+profiling"
+add_lit_testsuite(check-hpvm-torch-profiling "Run tests for torch frontend + profiling"
   ${CMAKE_CURRENT_BINARY_DIR}/test_profiling
   ARGS "-j1"  # Run DNN benchmarks sequentially
 )
@@ -33,7 +33,7 @@ configure_lit_site_cfg(
   MAIN_CONFIG
   ${CMAKE_CURRENT_SOURCE_DIR}/test_tuning/lit.cfg.py
 )
-add_lit_testsuite(check-hpvm-tuning "Run tests for frontend+autotuning"
+add_lit_testsuite(check-hpvm-torch-tuning "Run tests for torch frontend + autotuning"
   ${CMAKE_CURRENT_BINARY_DIR}/test_tuning
   ARGS "-j1"  # Run tuning tests sequentially
 )