Abstract: Understanding the brain is perhaps the greatest challenge facing twenty-first century science. Currently, there exists a tangible gap between the intelligence of computer systems and that of human beings. While a traditional von Neumann computer excels in precision and unbiased logic, its pattern recognition abilities lag far behind those of biological neural systems. However, exciting new technologies have emerged that possess high potential to bridge this gap. Furthermore, the fields of neuromorphic and brain-based robotics hold enormous promise for furthering our own understanding of the brain. Cloud robotics is a new paradigm in which robots take advantage of the Internet as a resource for massively parallel computation and real-time sharing of big data. A neuromorphic cloud infrastructure and its extensive set of internet-accessible resources has potential to provide significant benefits in myriad areas. In this paper we survey several current approaches to these technologies and propose a potential architecture for neuromorphic cloud robotics.

Keywords: Neuromorphic computing, Cloud robotics, Big data, Brain-based robots;

1. Introduction

To understand the human brain, it is essential to describe its different levels of organization, including gene expression, proteins and their interactions, cells, synaptic connections, neuronal microcircuits, areas and systems, and to understand the functional interactions within and between those different levels [25]. In this paper we look at three major transformations in Information Technology (IT): Cloud Robotics, Neuromorphic Computing  and Big Data which together could help decipher the workings of the human brain. We now introduce each of the three topics.

The rise of cloud computing and cloud data stores has been a precursor and facilitator to the emergence of big data. Cloud computing is the commodification of computing time and data storage by means of standardized technologies. The term “Cloud enabled Robotics” (CR), used for the first time by James Kuffner [27] presented the potential of distributed networks combined with service robots, primarily to enhance the robot agents limited capabilities. In 2011, Google and Willow Garage introduced their foreseen application of CR [28], which first demonstrated how to make robots smarter and more energy efficient.

Neuromorphic engineering, also known as Neuromorphic Computing (NC) [1][2][3] is a concept developed by Carver Mead [4] 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. NC has evolved significantly in the last couple of decades in terms of representing a wider concept that bridges computing systems and neural systems. Thus, NC is now an interdisciplinary subject, taking 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 upon those of biological nervous systems [5].  

While the initial attempts of NC were focused on “brain-centered” techniques such as perceptrons [7] and retinas [8], research has shifted to a more “hardware-centered” strategy with the advent of Neuromorphic robots. Neuromorphic robotics has the potential to provide the groundwork for the development of intelligent machines, thereby contributing to our understanding of the brain and how the nervous system gives rise to complex behaviour [6]. These robots are physical devices whose control system has been modelled after some aspect of brain processing. Neuromorphic robots are built on the notion that the brain is embodied in the body and the body is embedded in the environment. This embodiment mediates all motion and is critical for cognitive skills. Some of the open ended research questions include (a) how our mind is constructed from physical substrates such as brain and body, and (b) how complex systems such as the brain, give rise to intelligent behavior through the interactions with the world [9]. Thus, neuromorphic robots can provide both empirically and intuitively how the brain works.

Big data is being used by enterprises to discover facts that were previously unknown. Big data is not just about giant data volumes; it’s also about an extraordinary diversity of data types, delivered at various speeds and frequencies. It is estimated that about 2 zettabytes (or 10**21 bytes) of digital data is being generated every year by everything from underground physics experiments to retail transactions to security cameras to global positioning systems [36]. Advanced data analytic tools including those based on predictive analytics, data mining, statistics, artificial intelligence and natural language processing are being used for Big Data Analytics (BDA), which is one of the main practises in Business Intelligence (BI) today [37].

The remainder of the paper is organized as follows: we first examine several instances of neuromorphic engineering in section 2, and then survey current implementations of cloud robotics, following up with a review of a few successfully implemented robots that further our understanding of the brain, in section 3. In section 4, we discuss some of the challenges that currently exist in robotics and subsequently address them in section 5. We summarize our conclusions in section 6.

2. Neuromorphic Computing

Traditional von Neumann architectures have several advantages over the human brain, such as precision, indefatigability, logic, and lack of bias. In the realms of pattern recognition and power consumption however, a biological system far exceeds the capabilities of any traditional computer framework [34]. Neuromorphic computing’s mimicry of human neural networks aims to bridge the gap between these two disparate sets of capabilities. Several institutions, namely the Human Brain Project, Qualcomm, and IBM, have made forays into the realm of neuromorphic engineering via developing computer chips that utilize completely new architectures.

The European Union-funded Human Brain Project (HBP) is a collaborate scientific research project that aims to model the human brain [15]. One neuromorphic computing system component of the HBP is the SpiNNaker chip, which can simulate 16,000 neurons and eight million synapses. Each chip consists of several processors, acting as “fascicle processors”, that are able to model up to 1,000 neurons, receiving and emitting spike events [15]. Although the platform can be used for any application, it was designed to simulate neural systems, with the facility to evaluate different algorithms and thus, different types of neurons and connectivity patterns.
2.1 Neuromorphic Computing in Industry:

The market size of neuromorphic computing is expected to reach 6,480 million USD by 2024, according to a new study by Grand View Research, Inc [39]. The increasing demand for cognitive and brain-based robots is projected to impel growth within the industry, spurred on by the numerous benefits that neuromorphic chips can provide to users, including cognitive computing, optimum usage of memory, high-speed performance and low power consumption. The escalated demand within diverse industry verticals, including consumer electronics, automotives, and robotics, is instrumental in keeping the industry prospects upbeat. The global neuromorphic computing market is expected to gain traction, owing to the rising demand for artificial intelligence. Several institutions, namely Qualcomm, IBM, and Lawrence Livermore National Laboratory, have made forays into the realm of neuromorphic engineering via developing computer chips that utilize completely new architectures.