RRAM based processing-in-memory for efficient intelligent vision tasks at the edge

The work presents a proof-of-concept methodology for at edge visual data storage and processing-in-memory (PIM) as visual data preprocessing inspired from the biological visual system pipeline. This work proposes a methodology to improve the contrast of low-light low-contrast image by carefully modulating the conductance of memristive kind oxide-based resistive memory (RRAM)device. We present the level of contrast enhancement using conductance modulation of different non-filamentary RRAMs with different material stacks and also analyze the impact of RRAM variability on the contrast enhancement. For intelligent vision tasks, we implement artificial neural network (ANN) to perform the image classification and shows the best-case improvement of $\sim$ 1500 epochs ($\sim$ 74%) using RRAM based PIM. We also implement a large sized ANN “Efficient-Det Network” to perform object recognition on low-light low-contrast dataset ”Ex-Dark” to evaluate the proposed method using PIM layer. The result shows 8% higher mAP than network without a PIM layer. The present work is a step towards the development of efficient hybrid visual system for intelligent vision tasks at edge.