RVTensor: A Light-Weight Neural Network Inference Framework Based on the RISC-V Architecture

The open-source instruction set architecture RISC-V has developed rapidly in recent years, and its combination mode of multiple sub-instruction sets has attracted the attention of IoT vendors. However, research on the IoT scenario inference framework based on the RISC-V architecture is rare. Popular frame-works such as MXNet, TensorFlow, and Caffe are based on the X86 and ARM architectures, and they are not optimized for the IoT scenarios. We propose RVTensor that a light-weight neural network inference framework based on the RISC-V architecture. RVTensor is based on the SERVE.r platform and is optimized for resource-poor scenarios. Our experiments demonstrate that the accuracy of RVTensor and the Keras is the same.

[1]  Xu Wen,et al.  Improving RGB-D Face Recognition via Transfer Learning from a Pretrained 2D Network , 2019, Bench.

[2]  Zheng Zhang,et al.  MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems , 2015, ArXiv.

[3]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[5]  Minyi Guo,et al.  PSL: Exploiting Parallelism, Sparsity and Locality to Accelerate Matrix Factorization on x86 Platforms , 2019, Bench.

[6]  Zihan Jiang,et al.  Performance Analysis of Cambricon MLU100 , 2019, Bench.

[7]  Huiqian Niu,et al.  An Implementation of ResNet on the Classification of RGB-D Images , 2019, Bench.

[8]  Tianshu Hao,et al.  The Implementation and Optimization of Matrix Decomposition Based Collaborative Filtering Task on X86 Platform , 2019, Bench.

[9]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[10]  Maosen Chen,et al.  An Efficient Implementation of the ALS-WR Algorithm on x86 CPUs , 2019, Bench.

[11]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Guangli Li,et al.  XDN: Towards Efficient Inference of Residual Neural Networks on Cambricon Chips , 2019, Bench.

[13]  Fan Zhang,et al.  AIBench: Towards Scalable and Comprehensive Datacenter AI Benchmarking , 2018, Bench.