Optimising CNN Model on Low Power Vision DSP
March 27, 2024
The Client
The customer, an IP company, specializes in vision-based DSPs utilized for Imaging, Computer Vision, and AI applications.
Challenge
The project aimed to execute the end-to-end Inception-V3 CNN image classification ML model inference on the Customer’s Vision DSP.
Solution
The project utilized a range of tools and technologies, including C/C++, Quantization, CNN inference, DSP intrinsic, DMA, and tiling methodologies.
We had successfully identified the ImageNet dataset-based Inception-V3 floating point model, achieving Top-1 and Top-5 accuracy rates of 74% and 91.62% respectively. We then quantized the float model to the INT8 data type using McW’s custom quantization algorithm. Subsequently, an x86-based reference Inception-V3 pipeline was implemented for the INT8 data type.
Top-5 / Top-1 Classification Accuracy for Float vs. 8-Bit Quantized Graph
MulticoreWare hand-optimized various layers/operations in the Inception-V3 model for the Vision DSP, creating an end-to-end intrinsic-based pipeline while matching the accuracy with an x86-based INT8 pipeline. Considering the numerous layers in Inception-V3 and the DSP’s limited on-chip data memory, we had carefully designed and implemented DMA and data tiling algorithms to manage data transfer from external to on-chip memory efficiently.
Custom Quantization Logic:
MulticoreWare’s solution featured custom quantization logic with minimal loss in Top-1 and Top-5 classification accuracy for the quantized model. We hand-optimized approximately 94 layers of the Inception-V3 model using DSP intrinsic techniques, closely aligning with theoretical performance estimates. Additionally, our team implemented data tiling of input/output/weights and constructed an end-to-end Inception-V3 optimized pipeline, effectively hiding DMA data transfer latency.
CNN Model: Inception-V3 (Pre-Trained with Imagenet Dataset)
| Convolutional Neural Network Architecture Details | |
|---|---|
|
Number of Convolution layers |
94 |
|
Number of Concatenation layers |
11 |
|
Number of Pooling layers |
14 |
Business Impact
MulticoreWare’s efforts resulted in the customer achieving a processing speed of 30 FPS for input images sized at 299x299x3 while maintaining Top-1 and Top-5 accuracy levels similar to the float accuracy. This served as an excellent demonstration for the customer to showcase to their clients.
| Memory Modeling - DDR latency [clock cycles] | FPS |
|---|---|
|
100 |
30.42 |
|
0 |
31.09 |
Conclusion
This case study highlights MulticoreWare’s expertise in Quantization and DSPs. For a more comprehensive understanding of our solutions and services, please contact us at info@multicorewareinc.com
