Yilin Sun
6baf4427ce
Signed-off-by: Yilin Sun <imi415@imi.moe> |
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| .. | ||
| armgcc | ||
| source | ||
| board.c | ||
| board.h | ||
| clock_config.c | ||
| clock_config.h | ||
| cr_section_macros.h | ||
| dcd.c | ||
| dcd.h | ||
| deepviewrt_modelrunner_v3_14.xml | ||
| evkbimxrt1050_sdram_init.jlinkscript | ||
| pin_mux.c | ||
| pin_mux.h | ||
| readme.md | ||
readme.md
Overview
DeepView RT is an implements a neural network inference engine suitable for embedded processors, graphics processors, and microcontrollers. The DeepView RT Model (RTM) format supports in-place interpretation which allows it to be stored directly in flash and used as-is. This allows for resource constrained MCU (ex: Cortex-M) platforms as the actual model and its weights do not consume any RAM and instead can be kept in-place in flash memory. A small amount of memory is required for the network evaluation graph as well as a buffer cache for storing the volatile input/output data at inference time, but no memory is required for the actual weights which can remain in flash. If the cache is large enough to host the weights as well, they will be streamed from flash on-demand as a performance optimization, and for maximum performance on parts with adequate memory the entire model can be stored in RAM. DeepView RT ModelRunner is a dedicated service for hosting and evaluating RTM graphs through a set of RPC protocols, it provides an HTTP REST API for loading an evaluating models. It can work with eIQ toolkit to evaluating and profiling models. Note: DeepView RT supports only a subset of operators available in TensorFlow. The eIQ toolkit can convert Tensorflow 2.x model to RTM model, please refer to eIQ toolkit about model conversion.
Documentation
https://www.nxp.com/design/software/development-software/eiq-ml-development-environment:EIQ
Library configuration
Stack memory configuration During the library compilation, based on the stack memory configuration, the EIGEN_STACK_ALLOCATION_LIMIT macro definition can be set to the maximum size of temporary objects that can be allocated on the stack (they will be dynamically allocated instead). A high number may cause stack overflow. A low number may decrease object allocation performance.
Release notes
The library is based on DeepView RT version 2.4.44.
Limitations
- DeepView RT library depends on NewLib for MCUXpresso IDE.
SDK version
- Version: 2.15.000
Toolchain supported
- MCUXpresso 11.8.0
- IAR embedded Workbench 9.40.1
- Keil MDK 5.38.1
- GCC ARM Embedded 12.2
Hardware requirements
- Mini/micro USB cable
- EVKB-IMXRT1050 board
- Personal Computer
Board settings
No special settings are required.
Prepare the Demo
Connect a USB cable between the PC host and the OpenSDA(or USB to Serial) USB port on the target board. Open a serial terminal on PC for OpenSDA serial(or USB to Serial) device with these settings: - 115200 baud rate - 8 data bits - No parity - One stop bit - No flow control Insert the Ethernet Cable into the target board's RJ45 port and connect it to a router (or other DHCP server capable device). Download the program to the target board. Either press the reset button on your board or launch the debugger in your IDE to begin running the demo.
Running the demo
When the demo runs, the log would be seen on the terminal like:
Initializing PHY...
DHCP example
DHCP state : SELECTING DHCP state : REQUESTING DHCP state : CHECKING DHCP state : BOUND
IPv4 Address : 192.168.0.4 IPv4 Subnet mask : 255.255.255.0 IPv4 Gateway : 192.168.0.1
8.3 Refer to eIQ Toolkit User's Guide to validate or profiling the model