Ferroelectric FET Based In-Memory Computing for Few-Shot Learning

Abstract

As CMOS technology advances, the performance gap between the CPU and main has not improved. Furthermore, the hardware deployed for Internet of Things (IoT) applications need to process ever growing volumes of data, which can further exacerbate the memory wall. Computing-in-memory (CiM) architectures, where logic and arithmetic operations are performed in memory, can significantly reduce energy and latency overheads associated with data transfer, and potentially alleviate processor-memory bottlenecks. In this paper, we consider the utility of ternary content addressable (TCAM) arrays and CiM arrays based on ferroelectric field effect transistors (FeFETs) to support emerging machine learning models that can learn new classes of data with significantly less training overhead - highly desirable in IoT applications. Architecturally, we use TCAM and CiM arrays to implement the external module in a enhanced neural network (MENN) - which can be used to minimize catastrophic forgetting - a major problem in applications such as lifelong and few-shot learning. As a representative example, we achieve 95.14% accuracy for a few-shot learning task with the Omniglot data set by using a combined L∞ infinity and L1 distance metric computed via a TCAM-CiM cascaded architecture (as opposed to 99.06% accuracy assuming a GPU backed by DRAM). While there is a slight drop in accuracy, the TCAM-CiM approach is 4.34X faster and 4.18X more energy efficient than a CMOS implementation for the same task. The ability of an FeFET to serve as both a compact logic and storage element helps to enable dense CiM and TCAM structures that drive the aforementioned improvements to application-level figures of merit (FOMs).