The rapid advancement of artificial intelligence (AI) has been marked by the
large language models exhibiting human-like intelligence. However, these models
also present unprecedented challenges to energy consumption and environmental
sustainability. One promising solution is to revisit analogue computing, a
technique that predates digital computing and exploits emerging analogue
electronic devices, such as resistive memory, which features in-memory
computing, high scalability, and nonvolatility. However, analogue computing
still faces the same challenges as before: programming nonidealities and
expensive programming due to the underlying devices physics. Here, we report a
universal solution, software-hardware co-design using structural
plasticity-inspired edge pruning to optimize the topology of a randomly
weighted analogue resistive memory neural network. Software-wise, the topology
of a randomly weighted neural network is optimized by pruning connections
rather than precisely tuning resistive memory weights. Hardware-wise, we reveal
the physical origin of the programming stochasticity using transmission
electron microscopy, which is leveraged for large-scale and low-cost
implementation of an overparameterized random neural network containing
high-performance sub-networks. We implemented the co-design on a 40nm 256K
resistive memory macro, observing 17.3% and 19.9% accuracy improvements in
image and audio classification on FashionMNIST and Spoken digits datasets, as
well as 9.8% (2%) improvement in PR (ROC) in image segmentation on DRIVE
datasets, respectively. This is accompanied by 82.1%, 51.2%, and 99.8%
improvement in energy efficiency thanks to analogue in-memory computing. By
embracing the intrinsic stochasticity and in-memory computing, this work may
solve the biggest obstacle of analogue computing systems and thus unleash their
immense potential for next-generation AI hardware.

通过软硬件协同设计和内存计算优化随机加权模拟阻性存储神经网络的拓扑结构，提高了基于模拟计算的人工智能硬件的能效和分类性能。

优化模拟人工智能的随机阻性存储裁剪

Pruning random resistive memory for optimizing analogue AI

More than 3.5 billion people live in rural areas, where water and water
energy resources play an important role in ensuring sustainable and productive
rural economies. This article reviews and critically analyses the recent
advances in the field of analogue and reservoir computing that have been driven
by unique physical properties and energy of water waves. It also demonstrates
that analogue and reservoir computing hold the potential to bring artificial
intelligence closer to people living outside large cities, thus enabling them
to enjoy the benefits of novel technologies that already work in large cities
but are not readily available and suitable for regional communities.

本文综述和批判性分析了水波动的独特物理特性和能量驱动下模拟计算和水库计算领域的最近进展，并证明这些技术具有将人工智能引入大城市以外地区的潜力，从而使人们享受到在大城市已经行之有效的新技术所带来的种种好处。