The age of information relies on the scaling of CMOS technology, which allows the decrease of cost per component, and the improve of computing performance at each technology generation. However, CMOS scaling is slowing down due to inherent physical scaling problems, such as the short channel effects which raise the static power consumption and does not allow the downscaling of the dynamic power consumption. In addition, the transfer of data between the memory and the central processing unit (CPU) introduces a ‘memory wall’ issue, in terms of latency and additional power consumption. All these problems can be solved by novel in-memory computing architectures, which would totally suppress the memory wall and the static power consumption by using non-volatile switches for computing. A novel computer based on this concept would revolutionize the scenario of computing by enabling ultra-high density, ultra-low power logic and analog processors for several applications, from the Internet of Things (IoT), to neuromorphic processors for artificial intelligence.
This project aims at the development and demonstration of a new computer using resistive switch devices for computation and memory within an in-memory computing architecture. The project will address this broad objective from different standpoints, including (i) developing a novel generation of resistive switches with improved window and cycling endurance capability, (ii) develop a novel resistive-switch logic architecture serving as a universal platform for in-memory computing, and (iii) developing novel schemes for analog computing, including brain-inspired neuromorphic computing, by using resistive-switch technology.
Achieving these goals would result in a paradigm shift for the electronic industry and for society, by introducing a scalable technology of devices serving as both switches and memory, thus serving all in-memory applications in both the digital computers and analog systems, such as the neuromorphic networks for object learning and inference. For instance, the availability of non-volatile logic computing schemes will enable low-power microcomputers for the IoT, where event-driven computation takes place only in correspondence of sensory inputs. Massive in-memory computing architectures using digital/analog resistive switches would allow efficient processing of big data problems, such as data clustering and hardware learning accelerators. Analog neuromorphic systems with nanoscaled synapses will enable brain-inspired computation in robots and drones, and allow for low-power, high energy-efficient driverless cars.
