Holistic approaches to memory solutions for the Autonomous Driving Era

As DNNs improving state-of-the-art accuracy on many artificial intelligence (AI) applications such as computer vision processing for autonomous driving, the data processing bandwidth and power consumption between neural network accelerator and the off-chip memory are big challenge to enhance the compute performance metric TOPs/watt. To overcome the limited compute and energy resources in automobile environment, inferencing by PIM (Processing in Memory) or AiM (Accelerator in Memory) which deployed MAC(Multiply and Accumulation) units and activation function inside DRAM is one of the key solution by using multi bank parallelism and memory cell architecture. When memory technology equipped with analog logic inside mature in the near future, ultra-low power analog accelerator based neuromorphic computing architecture will lead the future autonomous driving solution.