Analog in-memory computing (AiMC) is an emerging technology that shows
fantastic performance superiority for neural network acceleration. However, as
the computational bit-width and scale increase, high-precision data conversion
and long-distance data routing will result in unacceptable energy and latency
overheads in the AiMC system. In this work, we focus on the potential of
in-charge computing and in-time interconnection and show an innovative AiMC
architecture, named AiDAC, with three key contributions: (1) AiDAC enhances
multibit computing efficiency and reduces data conversion times by grouping
capacitors technology; (2) AiDAC first adopts row drivers and column time
accumulators to achieve large-scale AiMC arrays integration while minimizing
the energy cost of data movements. (3) AiDAC is the first work to support
large-scale all-analog multibit vector-matrix multiplication (VMM) operations.
The evaluation shows that AiDAC maintains high-precision calculation (less than
0.79% total computing error) while also possessing excellent performance
features, such as high parallelism (up to 26.2TOPS), low latency (<20ns/VMM),
and high energy efficiency (123.8TOPS/W), for 8bits VMM with 1024 input
channels.

通过引入 In-charge computing 和 In-time interconnection 的创新 AiMC 架构 AiDAC，提高了多位计算效率，减少了数据转换时间，支持大规模的全模拟多位向量矩阵乘法运算，并保持高精度计算，同时具备高并行性、低延迟和高能效优势。

一种用于多位和大规模向量矩阵乘法的全模拟存储计算架构

An All-Analog in-Memory Computing Architecture for Multi-Bit and  Large-Scale Vector Matrix Multiplication

With the rapid evolution of the wind energy sector, there is an
ever-increasing need to create value from the vast amounts of data made
available both from within the domain, as well as from other sectors. This
article addresses the challenges faced by wind energy domain experts in
converting data into domain knowledge, connecting and integrating it with other
sources of knowledge, and making it available for use in next generation
artificially intelligent systems. To this end, this article highlights the role
that knowledge engineering can play in the process of digital transformation of
the wind energy sector. It presents the main concepts underpinning
Knowledge-Based Systems and summarises previous work in the areas of knowledge
engineering and knowledge representation in a manner that is relevant and
accessible to domain experts. A systematic analysis of the current
state-of-the-art on knowledge engineering in the wind energy domain is
performed, with available tools put into perspective by establishing the main
domain actors and their needs and identifying key problematic areas. Finally,
guidelines for further development and improvement are provided.

通过知识工程，将风能领域的数据转化为领域知识，与其他知识源相连接和整合，以应用于下一代人工智能系统的数字化转型，本文总结了知识工程的主要概念和风能领域的知识表示，分析了当前风能领域中知识工程的现状，并提供进一步发展和改进的指导。