The massive use of artificial neural networks (ANNs), increasingly popular in many areas of scientific computing, rapidly increases the energy consumption of modern high-performance computing systems. An appealing and possibly more sustainable alternative is provided by novel neuromorphic paradigms, which directly implement ANNs in hardware. However, little is known about the actual benefits of running ANNs on neuromorphic hardware for use cases in scientific computing. Here we present a methodology for measuring the energy cost and compute time for inference tasks with ANNs on conventional hardware. In addition, we have designed an architecture for these tasks and estimate the same metrics based on a state-of-the-art analog in-memory computing (AIMC) platform, one of the key paradigms in neuromorphic computing. Both methodologies are compared for a use case in quantum many-body physics in two dimensional condensed matter systems and for anomaly detection at 40 MHz rates at the Large Hadron Collider in particle physics. We find that AIMC can achieve up to one order of magnitude shorter computation times than conventional hardware, at an energy cost that is up to three orders of magnitude smaller. This suggests great potential for faster and more sustainable scientific computing with neuromorphic hardware.

翻译：人工神经网络在科学计算多个领域的广泛应用正迅速增加现代高性能计算系统的能耗。一种更具吸引力的可持续替代方案是基于新型神经形态范式，该范式在硬件层面直接实现人工神经网络。然而，对于在科学计算场景下将人工神经网络部署于神经形态硬件的实际效益，目前仍知之甚少。本文提出了一套在常规硬件上测量人工神经网络推理任务能耗与计算耗时的方法论。同时，我们针对此类任务设计了专用架构，并基于当前神经形态计算的核心范式之一——先进模拟内存计算平台，估算了相同的性能指标。两种方法论分别在二维凝聚态系统的量子多体物理应用场景及大型强子对撞机40兆赫兹速率异常检测任务中进行了比较。结果表明，模拟内存计算平台相比常规硬件可实现计算时长缩短一个数量级，同时能耗降低三个数量级。这预示着神经形态硬件在实现更快速、更可持续的科学计算方面具有巨大潜力。