A Robust Digital RRAM-Based Convolutional Block for Low-Power Image Processing and Learning Applications

Currently, there is a growing attention toward developing efficient hardware convolutional blocks for several applications such as computer vision or image processing. Recent works have shown that using binary values in convolutional blocks can considerably reduce the overall power consumption while achieving a high degree of accuracy. In parallel, some works employed resistive random-access memory (RRAM) as an in-memory accelerator to directly store the convolution kernels and perform analog dot product operations in the array, reducing the overall power consumption by limiting the number of memory accesses. However, such architecture is hampered by the limited resistance precision and large intrinsic variability of RRAMs. In this paper, we present a purely digital robust RRAM-based convolutional block using single-ended XNOR sensing capable of performing dot product operations in a single cycle. By carefully considering physical design and RRAM limitations at the 28-nm technology node, we show that at the circuit level, our architecture can tolerate a resistance window as low as 1.09, ensuring reliable operations even under a high RRAM variability (sigma/mu = 25% for a resistance window between both states around 50). When integrated in ISAAC, a state-of-the-art learning accelerator, our block can reduce the power by 2.7x while guaranteeing robust operations.