ANalogue In-Memory computing with Advanced device TEchnology

Descripción del proyecto

Racionalizar el tratamiento de datos para aumentar la eficiencia energética

Cada día se procesan enormes cantidades de datos digitales, lo que se traduce en un consumo de energía elevado. Los centros de datos gastan aproximadamente 200 teravatios por hora al año, lo que equivale al 1 % de la demanda mundial de energía. El equipo del proyecto ANIMATE, financiado por el Consejo Europeo de Investigación, propone crear un modelo de computación en memoria de bucle cerrado que será mucho más eficiente desde el punto de vista energético porque puede realizar cálculos algebraicos lineales en un solo paso computacional. Para validar el modelo se desarrollarán la tecnología de dispositivos, las topologías de circuitos, las arquitecturas de sistemas y la cartera de aplicaciones necesarias. Se prevé que este modelo tenga unas necesidades energéticas 5 000 veces inferiores a las de sus homólogos digitales de primera categoría.

Objetivo

Every day we generate, process and use a massive amount of data. Searching a keyword on the internet, choosing a movie for the weekend and booking our next holiday are just a few simple actions that rely on data-intensive algorithms in the cloud, such as data search, recommendation and page ranking. The energy cost of computation is high: it has been recently reported that training a relatively large neural network produces the same carbon dioxide of 5 cars in their whole lifetime. Data centres use an estimated 200 terawatt-hours each year, corresponding to 1% of the global demand. With the spectre of an energy-hungry future, it is essential to identify novel concepts, novel algorithms and novel hardware for streamlining the computing process.
My preliminary research has shown that computing energy requirements can be reduced by closed-loop in-memory computing (CL-IMC) that can solve linear algebra problems in just one computational step. In CL-IMC, the time to solve a certain problem does not increase with the problem size, in contrast to other computing concepts, such as digital and quantum computers. Thanks to the size-independent computing time around 100 ns, CL-IMC requires 5,000 times less energy than top-class digital computers at the same bit precision. These preliminary results show that CL-IMC is a promising new computing concept to reduce the energy consumption of data processing.
My project will develop the device technology, the circuit topologies, the system-level architectures and the application portfolio to fully validate the CL-IMC concept. A novel memory technology that is immune to wire resistance effects will be developed. CL-IMC integrated circuits will be designed with standard CMOS technology. System-level architecture and application exploration will further support the scalability and feasibility of the concept, to demonstrate CL-IMC as a primary contender among the computing technologies with improved energy efficiency.