Advancements of MEMristor Materials in Neuromorphic Computing for Autonomous Systems. 2020-01-0088
The advancements in analog electronics has spurred the development of neuromorphic computing which can replicate bio-neurological processes using artificial synapses. Artificial synapses can process information faster and more efficiently than CPUs for specialized applications like sparse coding, graph searches, and constraint-satisfaction problems. Neuromorphic systems offset CPU’s lack of processing power to solve complex tasks and computations, higher parallelism, novel neural-inspired algorithms, and optimizations. Neural-inspired algorithms such as sparse coding, simultaneous localization and mapping (SLAM), path planning, and object tracking event-based cameras are necessary in development of autonomous systems. As the industry and academia realizes the limitations posed Moore’s Law, new computing and performance by MEMristors has enabled continued process-node scaling. New technology like Intel’s inspired neuromorphic microchip demonstrates the benefits of a specialized architecture for emerging applications, including some of the computational problems hardest for the internet of things (IoT) and autonomous devices to support. As new complex computing workloads grow the need for specialized architectures designed for specific applications will be in demand. Specialized architectures using specific applications are ideal for real-world applications, from autonomous vehicles to smart homes to cybersecurity. This paper analyzes the current state-of-the-art materials, hardware, and applications used in developing neuromorphic computing. These materials and hardware achieve synaptic behaviors by categorizing the types of dielectric materials as metal oxides, organic materials, 2D materials, halide perovskites, and emerging materials. Memristor based CMOS Graphics Processing Unit, Field Programmable Gate Array, and Application Specific Integrated Circuit are being developed as dedicated artificial intelligence (AI) chips for Autonomous solutions.