A Universal, Analog, In-Memory Computing Primitive for Linear Algebra Using Memristors

Abstract

The increasing demand for data-intensive computing applications, such as artificial intelligence (AI) and more specifically machine learning (ML), raises the need for novel computing hardware architectures capable of massive parallelism in performing core algebraic operations. Among the new paradigms, in-memory computing (IMC) with analogue devices is attracting significant interest for its large-scale integration potential, together with unrivaled speed and energy performance. Here, we present a fully-analogue, universal primitive capable of executing linear algebra operations such as regression, generalized least-square minimization and linear system solution with and without preconditioning. We study the impact of the main circuit parameters on accuracy and bandwidth with analytical closed-form expressions and SPICE simulations. Scaling challenges due to parasitic resistance/capacitance and their impact on key parameters such as bandwidth and accuracy are discussed. Finally, a comparison with existing solvers belonging to the same IMC framework is made to assess advantages and disadvantages of the proposed circuit.