74 lines
2.2 KiB
Markdown
74 lines
2.2 KiB
Markdown
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Overview
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========
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Demonstrates inference for models compiled using the GLOW AOT tool.
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The network used in this is based on the CIFAR-10 example in Caffe2 [1] & [2].
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[1] https://github.com/caffe2/tutorials/blob/master/CIFAR10_Part1.ipynb
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[2] https://github.com/caffe2/tutorials/blob/master/CIFAR10_Part2.ipynb
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This project does not include the pre-trained model or the training script
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since Caffe2 framework is deprecated and lately has become part of PyTorch.
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This project example only includes the bundle (binary) generated after running
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the Glow AOT tool and is intended to be used as-is. If you want a step-by-step
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example of running the Glow AOT tool for a given model take a look at the
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LeNet MNIST Glow example.
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The neural network consists of 3 convolution layers interspersed by
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ReLU activation and max pooling layers, followed by a fully-connected layer
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at the end. The input to the network is a 32x32 pixel color image, which will
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be classified into one of the 10 output classes.
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Files:
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main.c - example source code
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timer.c - implementation of helper functions for measuring inference time
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timer.h - declarations of helper functions for measuring inference time
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Toolchains supported
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- MCUXpresso IDE
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- IAR Embedded Workbench for ARM
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- Keil uVision MDK
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- ArmGCC - GNU Tools ARM Embedded
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SDK version
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===========
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- Version: 2.15.000
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Toolchain supported
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===================
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- IAR embedded Workbench 9.40.1
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- Keil MDK 5.38.1
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- GCC ARM Embedded 12.2
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- MCUXpresso 11.8.0
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Hardware requirements
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=====================
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- Mini/micro USB cable
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- EVKB-IMXRT1050 board
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- Personal Computer
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Board settings
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==============
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No special settings are required.
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Prepare the Demo
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================
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1. Connect a USB cable between the host PC and the OpenSDA USB port on the target board.
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2. Open a serial terminal with the following settings:
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- 115200 baud rate
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- 8 data bits
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- No parity
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- One stop bit
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- No flow control
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3. Download the program to the target board.
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4. Either press the reset button on your board or launch the debugger in your IDE to begin running the demo.
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Running the demo
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================
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The log below shows the output of the Release version in the terminal window:
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Top1 class = 8 (ship)
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Confidence = 0.728
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Inference time = 24 (ms)
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