Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In this architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex and unstructured data as our brain does. Neuromorphic computing systems are aimed at addressing these needs. The human brain performs about 10^15 calculations per second using 20W and a 1.2L volume. By taking inspiration from biology, new generation computers could have much lower power consumption than conventional processors, could exploit integrated non-volatile memory and logic, and could be explicitly designed to support dynamic learning in the context of complex and unstructured data. Among their potential future applications, business, health care, social security, disease and viruses spreading control might be the most impactful at societal level. This roadmap envisages the potential applications of neuromorphic materials in cutting edge technologies and focuses on the design and fabrication of artificial neural systems. The contents of this roadmap will highlight the interdisciplinary nature of this activity which takes inspiration from biology, physics, mathematics, computer science and engineering. This will provide a roadmap to explore and consolidate new technology behind both present and future applications in many technologically relevant areas.

中文翻译：

---

2021年神经形态计算与工程路线图

如今，基于冯·诺依曼架构的现代计算已成为一门成熟的前沿科学。在这种体系结构中，处理和存储单元被实现为单独的块，可以密集且连续地交换数据。这种数据传输是功耗的很大一部分。下一代计算机技术有望解决万亿级的问题。即使这些未来的计算机将具有令人难以置信的强大功能，但是如果它们基于von Neumann类型的体系结构，它们将消耗20到30兆瓦的功率，并且将不具有固有的物理内置功能来学习或处理复杂的非结构化数据，例如我们的大脑做到了。神经形态计算系统旨在满足这些需求。人脑使用20W和1.2L的体积每秒执行约10 ^ 15次计算。通过从生物学中获得启发，新一代计算机的功耗可以比传统处理器低得多，可以利用集成的非易失性存储器和逻辑，并且可以明确地设计为在复杂和非结构化数据的情况下支持动态学习。在其潜在的未来应用中，商业，医疗保健，社会保障，疾病和病毒的传播控制在社会层面可能是最有影响力的。该路线图设想了神经形态材料在前沿技术中的潜在应用，并将重点放在人工神经系统的设计和制造上。该路线图的内容将突出这一活动的跨学科性质，该活动的灵感来自生物学，物理学，数学，计算机科学和工程学。