Title NEUROWARE: Efficient and Robust Hardware for Brain-Inspired Computing (A project of the Knowledge Generation National Program)
Leader Marisa López Vallejo y Pablo Ituero
Strategic Areas Future Telecomms
Funding Entity Ministerio de Ciencia, Innovación y Universidades
The NEUROWARE project deals with one of the topics with most intensive activity for these and coming years: big data processing, and in particular the area of efficient brain-inspired computing. Many applications require deep learning algorithms to be computed near to the source of information, what imposes strict restrictions in terms of memory, computational costs and energy consumption along with adaptability to the source environment. However, there is a growing gap between the needs of algorithms designed by data scientist and the capabilities of the hardware available to accommodate them. These efficiency and energy gaps are not bridged by the technology reduction imposed by Moore’s Law, there is a need for novel circuital and architectural paradigms.
In NEUROWARE we propose hardware architectures for brain-inspired computing that contribute to increase the efficiency of current solutions to provide better performance and reduced energy consumption. The challenge is to implement reliable intelligence in edge devices and platforms in a way that is efficient in terms of performance and power. The general objectives of this project address some of these issues from the device, circuit and architectural levels. NEUROWARE plans to explore the architectural limits of circuits implementing the basic processing element in deep neural networks (DNN), the artificial neuron.