index ed6564d2dbb9fb17712509fb414f0e29f5af527d..7036d8dff87c1169aaf00b37be9cab339e1bc433 100644 (file)
--- a/docs/source/example.rst
+++ b/docs/source/example.rst
We ship three end-to-end examples within the tidl-api package
to demonstrate three categories of deep learning networks. The first
-two examples can run on AM57x SoCs with either DLA or DSP. The last
-example requires AM57x SoCs with both DLA and DSP. The performance
+two examples can run on AM57x SoCs with either EVE or DSP. The last
+example requires AM57x SoCs with both EVE and DSP. The performance
numbers that we present here were obtained on an AM5729 EVM, which
-includes 2 ARM A15 cores running at 1.5GHz, 4 DLA cores at 535MHz, and
+includes 2 ARM A15 cores running at 1.5GHz, 4 EVE cores at 535MHz, and
2 DSP cores at 750MHz.
Imagenet
as the most likely objects that the input image can be.
The following figure and tables shows an input image, top 5 predicted
-objects as output, and the processing time on either DLA or DSP.
+objects as output, and the processing time on either EVE or DSP.
.. image:: ../../examples/test/testvecs/input/objects/cat-pet-animal-domestic-104827.jpeg
:width: 600
====================== ==================== ============
Device Processing Time Host Processing Time API Overhead
====================== ==================== ============
- DLA: 123.1 ms 124.7 ms 1.34 %
+ EVE: 123.1 ms 124.7 ms 1.34 %
**OR**
DSP: 117.9 ms 119.3 ms 1.14 %
====================== ==================== ============
The particular network that we ran in this category, jacintonet11v2,
-has 14 layers. User can specify whether to run the network on DLA or DSP
-for acceleration. We can see that DLA time is slightly higher than DSP time.
+has 14 layers. User can specify whether to run the network on EVE or DSP
+for acceleration. We can see that EVE time is slightly higher than DSP time.
Host time includes the OpenCL runtime overhead and the time to copy user
input data into padded TIDL buffers. We can see that the overall overhead
is less than 1.5%.
The network we ran in this category is jsegnet21v2, which has 26 layers.
From the reported time in the following table, we can see that this network
-runs significantly faster on DLA than on DSP.
+runs significantly faster on EVE than on DSP.
.. table::
====================== ==================== ============
Device Processing Time Host Processing Time API Overhead
====================== ==================== ============
- DLA: 296.5 ms 303.3 ms 2.26 %
+ EVE: 296.5 ms 303.3 ms 2.26 %
**OR**
DSP: 812.0 ms 818.4 ms 0.79 %
====================== ==================== ============
.. image:: images/pexels-photo-378570-ssd.jpg
:width: 600
-The network can be run entirely on either DLA or DSP. But the best
-performance comes with running the first 30 layers on DLA and the
+The network can be run entirely on either EVE or DSP. But the best
+performance comes with running the first 30 layers on EVE and the
next 13 layers on DSP, for this particular jdetnet_ssd network.
Note the **AND** in the following table for the reported time.
Our end-to-end example shows how easy it is to assign a layers group id
====================== ==================== ============
Device Processing Time Host Processing Time API Overhead
====================== ==================== ============
- DLA: 175.2 ms 179.1 ms 2.14 %
+ EVE: 175.2 ms 179.1 ms 2.14 %
**AND**
DSP: 21.1 ms 22.3 ms 5.62 %
====================== ==================== ============
root@am57xx-evm:/usr/share/ti/tidl-api/examples/segmentation# cd ../ssd_multibox/; make -j4
root@am57xx-evm:/usr/share/ti/tidl-api/examples/ssd_multibox# ./ssd_multibox -i ../test/testvecs/input/roads/pexels-photo-378570.jpeg
Input: ../test/testvecs/input/roads/pexels-photo-378570.jpeg
- frame[0]: Time on DLA: 175.2ms, host: 179ms API overhead: 2.1 %
+ frame[0]: Time on EVE: 175.2ms, host: 179ms API overhead: 2.1 %
frame[0]: Time on DSP: 21.06ms, host: 22.43ms API overhead: 6.08 %
Saving frame 0 with SSD multiboxes to: multibox_0.png
Loop total time (including read/write/print/etc): 423.8ms