Editorial: In-memory sensing and computing: New materials and devices meet new challenges

Abstract

With the convergence of Artificial Intelligence (AI) and Internet of Things (IoT) redefining the way industries, business, and economies function, the demand for energyefficient and high-performance computing at the edge is exponentially increasing. Inspired by the low power and parallel processing capabilities of the biological brain, Neuromorphic Computing is an emerging computing paradigm that overcomes many limitations of the conventional computer architecture. Most importantly, by performing computations in-memory, Neuromorphic Computing overcomes the von Neuman bottleneck, thus improving the computational capability along with additional area and power savings. While several stand-alone neuromorphic chips have been developed with excellent energy efficiency for running specific AI algorithms, such digital systems still suffer when interfaced with edge sensors. This is because the sensory inputs are nonstructural, non-normalized, and fragmented, which incur large energy, time and wiring overheads on digital systems with separated sensing and processing units. This calls for in-memory sensing technologies, with fused sensing, memory, and processing capabilities, to unleash the full potential of highly sophisticated sensor and actuator systems used in bioelectronics and robotics.