On-chip sparse learning with resistive cross-point array architecture

Unsupervised learning with sparse coding is widely adopted in applications of feature extraction, pattern classification, and compressive sensing. However, even with the state-of-the-art hardware platform of CPUs/GPUs, solving a sparse coding problem is still expensive in computation. In this paper, the resistive cross-point array architecture (CPA) is proposed to achieve on-chip acceleration of sparse coding, especially the matrix/vector operations that are intensively used in the algorithm. Learning and recognition experiments are conducted with the MNIST handwriting dataset. By co-optimizing the algorithm, architecture, circuit, and resistive synaptic devices, SPICE simulation at 65nm demonstrates that the CPA is able to accelerate sparse coding computation by more than 3800X, compared to software running on an 8-core CPU. Furthermore, this work investigates the technological limitations of a realistic resistive CPA, including reduced ON/OFF range of synaptic devices, nonlinearity in programming, spatial and temporal variations, and interconnect parasitics. The results illustrate both enormous opportunities and practical barriers of resistive CPA in real-time learning on a chip.