Integrated circuits (ICs) and optoelectronic chips are the foundation stones of the modern
information society. The IC industry has been driven by the so-called “Moore's law” in the …

As von Neumann computing architectures become increasingly constrained by data-
movement overheads, researchers have started exploring in-memory computing (IMC) …

The “memory wall” problem or so-called von Neumann bottleneck limits the efficiency of
conventional computer architectures, which move data from memory to CPU for …

We propose a co-design approach for compute-in-memory inference for deep neural
networks (DNN). We use multiplication-free function approximators based on l 1 norm along …

Data movement overheads caused by the recent explosion in big data applications have
made traditional von Neumann architecture fails to tackle big data workloads. Processing in …

This paper discusses the potential of model-hardware co-design to simplify the
implementation complexity of compute-in-SRAM deep learning considerably. Although …

Binary neural networks (BNNs) are adequate for energy-constrained embedded systems
thanks to binarized parameters. Several researchers have proposed the compute-in …

Due to high data acquisition rates and large data volumes, on-board hyperspectral imagery
(HSI) demands data compression before transmission. The major bottleneck in on-board …

AI running on the edge requires silicon that can meet demanding performance requirements
while meeting the aggressive power and area budget. Frequently updated AI algorithms …

基于数据驱动的人工智能方法在电力系统运维实际应用中展示出相当的优势,
但目前人工智能技术在电力系统中还未能实现泛在落地应用, 其关键原因之一为电力智能计算 …