Optimizing convolutional neural network on DSP
2016
Deep learning techniques like Convolutional Neural Networks (CNN) are getting traction for classification of objects (e.g. traffic signs, pedestrian, vehicles etc.) in Advanced Driver Assistance Systems (ADAS). Typical CNN based trained networks poses huge computational complexity in feed forward path during operation due to multiple layers and within layer operations like 2D convolution, spatial pooling and non-linear mapping. The paper proposes optimization techniques to efficiently map such networks on Digital Signal processors (DSP). These techniques consist of fixed point conversion, data re-organization, weight placement and LUT usage resulting in optimal utilization of resources on C66xTM DSP. The proposed kernels are developed and simulated on Texas Instruments (TI)'s Driver Assist TDA3X platform with optimal utilization of compute and data resources inside DSP. These optimization techniques are applicable for multiple network topologies published in the literature.
Keywords:
- Deep learning
- Advanced driver assistance systems
- Computer vision
- Convolutional neural network
- Digital signal processor
- Digital signal processing
- Real-time computing
- Artificial neural network
- Computer science
- Network topology
- Artificial intelligence
- Lookup table
- Theoretical computer science
- Computational complexity theory
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
4
References
7
Citations
NaN
KQI