Overcoming the Limitations of Si-CMOS and Von Neumann Architectures by Adopting beyond-Si devices and Near-memory Computation

The advancement of Silicon CMOS technology has led to information technology innovation for decades. Transistor density, computation frequency, and storage capacity have been increased by several orders of magnitude, contributing to the thriving of artificial intelligence, cloud computing, the Internet of Things (IoT), etc. However, current Si CMOS technology is confronting several challenges. First, scaling transistors down according to Moore’s law almost reaches its limitations. The memory-wall problem in Von Neumann architecture is also worsening due to the growing performance disparity between processors and memory, as well as the emergence of memory-intensive applications. Second, the increasing need for computing necessitates the application of digital technologies in extreme environments and conditions where Si CMOS is not functional. To tackle these limitations and lead further innovations, this dissertation explores the utilization of emerging beyond-Si devices and near-memory computing to address the limitations of Si CMOS technology and Von Neumann Architecture.
First, this thesis introduces Silicon Carbide (SiC) processors for extremely high-temperature Venus surface exploration. Second, this thesis describes RecPIM: a PIM-enabled DRAM-RRAM hybrid memory system for accelerating deep learning recommendation models. Lastly, this thesis proposes a Near-Memory Processing (NMP) acceleration for scalable de novo genome assembly, which demands a large memory footprint and is highly memory-latency intensive.

CHAIR: Professor Ronald G. Dreslinski