Compute-in-memory (CiM) is one promising solution to address the memory bottleneck
existing in traditional computing architectures. However, the tradeoff between energy …

With the rapid advancement of artificial intelligence (AI), computing-in-memory (CIM)
structure is proposed to improve energy efficiency (EF). However, previous CIMs often rely …

For the non-stop demands for a better and smarter society, the number of electronic devices
keeps increasing exponentially; and the computation power, communication data rate, smart …

The rapid growth of CMOS logic circuits has surpassed the advancements in memory
access, leading to significant “memory wall” bottlenecks, particularly in artificial intelligence …

Advanced artificial-intelligence (Al) edge devices require high energy-efficiency (E) and high
inference-accuracy 2, 4-6. An SRAM-based compute-in-memory (CIM) based on MAC …

This article presents a programmable in-memory computing accelerator (PIMCA) for low-
precision (1–2 b) deep neural network (DNN) inference. The custom 10T1C bitcell in the in …

Cloud AI acceleration has drawn great attention in recent years, as big models are
becoming a popular trend in deep learning. Cloud AI runs high-efficiency inference, high …

Computing-in-memory (CIM) has shown high energy efficiency on low-precision integer
multiply-accumulate (MAC)[1–3]. However, implementing floating-point (FP) operations …

Transformer models achieve excellent results in the fields like natural language processing,
computer vision, and bioinformatics. Their large numbers of matrix multiplications (MMs) …

Advanced artificial intelligence edge devices are expected to support floating-point (FP)
multiply and accumulation operations while ensuring high energy efficiency and high …