New York, United States, Sat, 29 Feb 2020 00:27:27 / Comserve Inc. / -- The business-related trends driving the product consumption are discussed in detail in the report along with industry expertise to minimize the barriers to Artificial Intelligence Insight Series Market growth The application of Artificial Intelligence (NYSE:AI) is growing exponentially. This rapid expansion of AI software development, therefore, calls for a focused effort to build new hardware that can process the emerging AI algorithms. Future of AI hardware will be defined by biologically-inspired neuromorphic chipsets, which provide a real time boost for AI systems. Brain-like chips deliver natural intelligence in major AI applications in the long-term, and have the desirable characteristics of intelligent sensors. The ultimate aim is to develop process technologies, materials, memories, and other building blocks for the integration of the neuron chips into sensors.
As Moore's Law is coming to an end, silicon processing technology is being pushed to its limits. Of course, there have been a number of proposed methods to expand the computational power of traditional processors. Yet, at the moment, chipmakers are milking silicon for all its worth from 14nm to 10nm to 7nm silicon transistors and beyond. The way we are going, it won't be long until it's physically impossible to cram anything more onto chipsets. In short, we just can't continue going down the miniaturization path forever.
When Apple CEO Tim Cook introduced the iPhone X, he claimed it would "set the path for technology for the next decade." While it is too early to tell, the neural engine used for face recognition was the first of its kind. Today deep neural networks are a reality, and neuromorphic appears to be the only practical path to make continuing progress in AI. Facing data bandwidth constraints and ever-rising computational requirements, sensing and computing must reinvent themselves by mimicking neurobiological architectures, claimed a recently published report by Yole Développement (Lyon, France). In an interview with EE Times, Pierre Cambou, Principal Analyst for Imaging at Yole, explained that neuromorphic sensing and computing could solve most of AI's current issues while opening new application perspectives in the next decades.
Neuromorphic computing or neuromorphic engineering has been described as the use of large integration systems containing numerous analog circuits allowing the replication of neuro-biological behaviors existing in a human's nervous system. The neuromorphic computing market platform consists of two vital systems based on the custom hardware architecture. Such systems are designed to program neural microcircuits by applying brain-like thought process in cognitive computing and machine learning process. This procedure enables a machine to learn, adapt and function like a human brain does rather than functioning like a normal computer. In addition, to perform such a complex task, the computing platform requires the state-of-the-art circuit technologies and electronic components, which allows the platform to receive new data or knowledge gained from various other sources of neuroscience research, e.g.
Neuromorphic engineering, also known as neuromorphic computing, is a concept developed by Carver Mead, in the late 1980s, describing the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system. In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems (for perception, motor control, or multisensory integration). The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors, spintronic memories, threshold switches, and transistors. A key aspect of neuromorphic engineering is understanding how the morphology of individual neurons, circuits, applications, and overall architectures creates desirable computations, affects how information is represented, influences robustness to damage, incorporates learning and development, adapts to local change (plasticity), and facilitates evolutionary change. Neuromorphic engineering is an interdisciplinary subject that takes inspiration from biology, physics, mathematics, computer science, and electronic engineering to design artificial neural systems, such as vision systems, head-eye systems, auditory processors, and autonomous robots, whose physical architecture and design principles are based on those of biological nervous systems.