Artificial intelligence has made remarkable advancements in recent years, leading to the
development of algorithms and models capable of handling ever-increasing amounts of …

Deep random forest (DRF), which combines deep learning and random forest, exhibits
comparable accuracy, interpretability, low memory and computational overhead to deep …

To fully exploit the ferroelectric field effect transistor (FeFET) as compact embedded
nonvolatile memory for various computing and storage applications, it is desirable to use a …

In this work, we focused on the performance optimization of the neural network (NN) system
in the synaptic device of HfAlOx (HAO)-based ferroelectric field-transistors (FeFET) …

In this work, we propose a ferroelectric FET (FeFET) time-domain compute-in-memory (TD-
CiM) array as a homogeneous processing fabric for binary multiplication-accumulation …

Field programmable gate array (FPGA) is widely used in the acceleration of deep learning
applications because of its reconfigurability, flexibility, and fast time-to-market. However …

Analog weight cells based on ferroelectric field-effect transistors (FeFETs) are promising for
fast and energy efficient compute-in-memory (CIM) accelerators, yet their on-chip training is …

近年来, 物联网和人工智能等技术的发展对片上存储与智能计算的能效, 密度以及性能提出了更
高的要求. 面对传统CMOS 处理器的能效与密度瓶颈, 以及传统冯· 诺伊曼架构的“存储墙” 瓶颈 …

Matrix-vector multiplication (MVM) and content-based search are two key operations in
many machine learning workloads. This paper proposes a ferroelectric FET (FeFET) time …

Non-volatile memories (NVMs) have the potential to reshape next-generation memory
systems because of their promising properties of near-zero leakage power consumption …