Computation-in-Memory Accelerators for Secure Graph Database: Opportunities and Challenges

Abstract

This work presents the challenges and opportunities for developing computing-in-memory (CIM) accelerators to support secure graph databases (GDB). First, we examine the database backend of common GDBs to understand the feasibility of CIM-based hardware architectures to speed up database queries. Then, we explore standard accelerator designs for graph computation. Next, we present the security issues of graph databases and survey how advanced cryptographic techniques such as homomorphic encryption and zero-knowledge protocols can execute privacy-preserving queries in a secure graph database. After that, we illustrate possible CIM architectures for integrating secure computation with GDB acceleration. Finally, we discuss the design overheads, useability, and potential challenges for building CIM-based accelerators for supporting data-centric calculations. Overall, we find that computing-in-memory primitives have the potential to play a crucial role in realizing the next generation of fast and secure graph databases.