Quantifying the Benefits of Monolithic 3D Computing Systems Enabled by TFT and RRAM

Abstract

Current data-centric workloads, such as deep learning, expose the memory-access inefficiencies of current computing systems. Monolithic 3D integration can overcome this limitation by leveraging fine-grained and dense vertical connectivity to enable massively-concurrent accesses between compute and memory units. Thin-Film Transistors (TFTs) and Resistive RAM (RRAM) naturally enable monolithic 3D integration as they are fabricated in low temperature (a crucial requirement). In this paper, we explore ZnO-based TFTs and HfO2-based RRAM to build a 1TFT-1R memory subsystem in the upper tiers. The TFT-based memory subsystem is stacked on top of a Si-FET bottom tier that can include compute units and SRAM. System-level simulations for various deep learning workloads show that our TFT-based monolithic 3D system achieves up to 11.4× system-level energy-delay product benefits compared to 2D baseline with off-chip DRAM—5.8× benefits over interposer-based 2.5D integration and 1.25× over 3D stacking of RRAM on silicon using through-silicon vias. These gains are achieved despite the low density of TFT-based RRAM and the higher energy consumption versus 3D stacking with RRAM, due to inherent TFT limitations.