FeMAT: Exploring In-Memory Processing in Multifunctional FeFET-Based Memory Array

Abstract

The performance gap between the processors and the main memory is continuously widening, known as the memory wall bottleneck. Emerging nonvolatile devices have the ability of in-memory processing, and thus, have the potential to partially alleviate the memory wall bottleneck. People have adopted nonvolatile devices to build various accelerators that are targeted at different problems and applications. In this work, we adopt one of the emerging nonvolatile devices, the ferroelectric field-effect transistor (FeFET), to build a multifunctional in-memory processing unit, which is named FeMAT. From a structural point of view, FeMAT is an FeFET-based memory array composed of 3T-based cells. From a functional point of view, FeMAT not only is a nonvolatile memory, but also can perform some logic operations (i.e., the processing-in-memory (PIM) mode), binary convolutions (i.e., the binary convolutional neural network (BCNN) acceleration mode) and content searching (i.e., the ternary content-addressable memory (TCAM) mode) in the memory. These functions are seamlessly fused into the FeFET-based memory array and can be configured online without changing the circuit structure. Superior energy efficiency is demonstrated by our experiments and comparisons with a resistive random-access memory (ReRAM) based equivalence, as well as a TCAM and a BCNN accelerator based on complementary metal-oxide-semiconductor (CMOS) devices.