On-line machine learning accelerator on digital RRAM-crossbar

Leibin Ni, Hantao Huang, Hao Yu

Abstract: On-line machine learning has become the need for future data analytics. This work will show an ℓ2 norm based hardware solver for on-line machine learning that can significantly reduce training time when compared to the traditional gradient-based solution using backward propagation. We will show that the intensive matrix-vector multiplication in ℓ2 norm solution can be mapped onto a distributed in-memory accelerator using the recent resistive switching random access memory (RRAM) device. A digitized matrix-vector multiplication accelerator will be developed based on the distributed RRAM-crossbar. Such a distributed RRAM-crossbar architecture can utilize the reformulated ℓ2 norm solver with a scalable and energy-efficient solution for real-time training and testing in image recognition. Experiment results have shown that significant speedup can be achieved for matrix-vector multiplication in the ℓ2 norm solver such hat the overall training and testing time can be reduced respectively. In addition, large energy saving can be also achieved when compared to the traditional CMOS-based out-of-memory computing architecture.