doc: add compiler features and LP support doc

Signed-off-by: Prashant Gaikwad <[email protected]>

CompilerFeatures.md

@@ -0,0 +1,52 @@
+# DLA Compiler
+
+### Layers and features support
+
+|Layer                                        |Feature                                    |FP16                  |INT8                  |
+|-----------|---------------|-------|-------|
+|**Convolution**||✔|✔|
+||Dilation|✔|✔|
+||Winograd|✔|Not implemented in SW|
+|**Deconvolution**||✔|✔|
+||With padding|Not implemented in SW|Not implemented in SW|
+||Winograd|Not implemented in SW|Not implemented in SW|
+|**Fully Connected**||✔|✔|
+||Winograd|Not implemented in SW|Not implemented in SW|
+|**Group Convolution**||✔|Not implemented in SW|
+||Winograd|✔|Not implemented in SW|
+|**Pooling**||✔|✔|
+||Max|✔|✔|
+||Min|✔|✔|
+||Avg|✔|✔|
+||Inclusive padding|✔|✔|
+||Exclusive padding|Not supported in HW| Not supported in HW|
+|**Activation**||||
+||Bias|✔|✔|
+||BatchNorm|✔|✔|
+||Scale|✔|✔|
+||Sigmoid|✔|Not implemented in SW|
+||Tanh|✔|Not implemented in SW|
+||EltWise SUM|✔|✔|
+||EltWise SUB|Not supported in HW|Not supported in HW|
+||EltWise MIN|✔|Not implemented in SW|
+||EltWise MAX|✔|Not implemented in SW|
+|**LRN**||✔|Not implemented in SW|
+
+### Networks verification report
+
+|Network                                  |Configuration                          |fp16                   |int8                   |
+|-------|----|----|----|
+|MNIST|nv_full,nv_large,nv_small|Verified|Verified|
+|ResNet-18|nv_full,nv_large,nv_small|Verified|Verified|
+|ResNet-50|nv_full,nv_large,nv_small|Verified|Verified|
+
+### Known limitations
+- Not supported in HW
+    - Dilation with Winograd
+    - EltWise SUB
+    - Pooling and convolution layers where pad size is greater than kernel size
+- Not implemented in SW
+    - Deconvolution with strides > 32
+    - Deconvolution with input/output padding
+
+

LowPrecision.md

@@ -0,0 +1,81 @@
+# Low precision support in NVDLA
+
+Use of low precision such 8-bit, 4-bit, or even lower number of bits for inference is one of the optimization methods used in deep learning. NVDLA architecture includes INT8 (8-bit) precision support. It helps to compress the model reducing memory footprint and to improve performance with a small degradation in accuracy. Using INT8 precision for inference requires quantizing pre-trained models from floating point to INT8 and programming converters in NVDLA for scaling/re-scaling tensors.
+
+### NVDLA architecture for INT8 precision support includes the following:
+-	INT8 input/output data read/write
+-	32-bit internal pipeline, avoids saturation in mathematical computations
+-	Per-tensor input scaling using input converters
+-	Per-tensor and per-kernel output re-scaling using output converters
+
+### Steps to generate INT8 quantized model:
+-	Analyze the dynamic range of per-layer tensors and calculate scale factors
+-	Quantize model weights and determine the converter parameters using scale factors
+
+#### Analyze dynamic range of per-layer tensors and calculate scale factors
+A calibration tool can collect the dynamic range of the output tensor for each layer over a dataset of images. This dynamic range information can be used to calculate per-tensor scale factors. The NVDLA Compiler uses the following JSON schema to import scale factors.
+
+##### JSON schema for calibration table
+
+```
+{
+    "type" : "object",
+    "description": "JSON schema for calibration table",
+    "layer" : {
+        "type": "array",
+        "description": "per-layer scale factor for output tensor, scale factor can be described using either scale or min/max",
+        "oneOf": ["scale", {"min", "max"}],
+        "scale": {
+            "type": "float",
+            "description": "scale value calibrated for output tensor of layer"
+        },
+        "min": {
+            "type": float",
+            "description": "minimum value of the source precision dynamic range for output tensor of layer"
+        },
+        "max": {
+            "type": "float",
+            "description": "maximum value of the source precision dynamic range for output tensor of layer"
+        },
+        "offset": {
+            "type" : "integer",
+            "description": "offset used for asymmetric scaling, it should be 0 for symmetric scaling"
+        }
+    }
+}
+```
+
+##### Sample calibration table for first few layers of ResNet-50 using symmetric scaling
+
+```
+{
+	"data" : {
+		"scale": 0.00781453,
+		"min": 0,
+		"max": 0,
+		"offset": 0
+	},
+	"conv1" : {
+		"scale": 0.0891214,
+		"min": 0,
+		"max": 0,
+		"offset": 0
+	},
+	"pool1" : {
+		"scale": 0.0891214,
+		"min": 0,
+		"max": 0,
+		"offset": 0
+	},
+	"res2a_branch1" : {
+		"scale": 0.119546,
+		"min": 0,
+		"max": 0,
+		"offset": 0
+	}
+}
+```
+
+#### Quantize model weights and determine the converter parameters
+
+The NVDLA Compiler has the ability to quantize model weights and determine the converter parameters using the scale factors from the calibration table.