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.
