XIV Международная научно-техническая конференция АПЭП – 2018 On Usage of Neuromorphic Engineering in Autonomous Robots Andrey V. Gavrilov Novosibirsk State Technical University, Novosibirsk, Russia Abstract – The article considers the opportunities and problems II. PROBLEM DEFINITION of using neuromorphic engineering in intelligent autonomous robots. The digital and digital-analog kinds of neurochip The most known and advanced existing implementation architecture for usage in robotics are discussed, in particular, (for example, TrueNorth of IBM) have very limited three kinds of digital architecture: hard architecture, flexible opportunities for continuous learning with the long-term architecture and growth architecture of neural networks. work of an autonomous robot. They require preliminary training on the host computer and firmware into the Index Terms – Intelligent robots, autonomous robots, artificial neurochip already trained neural network [14]. If the intelligence, neural networks, neuromorphic engineering, neurochip architecture is able to work with external RAM to machine learning. store the network parameters in the learning process, then power consumption increases, and the advantage of a spiking I. INTRODUCTION neurochip may be lost. Approaches, algorithms, methods and tools associated with the creation of hardware spiking neural T PRESENT, service robotics [1] is widely used to networks are usually called neuromorphic engineering. It is serve people in public places, hospitals and at home. A possible to apply this term in a broad sense, to all Distinctive feature of service robots is human-like behavior technologies of neural networks existing at the present time. both from the point of view of kinematics and dynamics of But in this article we will adhere to a more narrow and movements, providing the possibility of the robot's activity in generally accepted interpretation. the "human" environment, and from the point of view of In further sections, the article gives a brief description of perception of the external environment (including the types of hardware spiking neural networks, analyzes their communication with people) and decision-making. advantages and disadvantages, suggests the classification of Therefore, research in the field of constructing artificial robots from the point of view of the applicability of general intelligence (AGI) [2, 3], is closely connected with neuromorphic engineering for them, suggests the architecture this direction of robotics, i.e. AI, capable of learning to solve of a hybrid robot control system based on hardware spiking any problems, like a human brain. neural networks. At the present time, hopes and plans for the creation of an AGI are increasingly associated with success in the development and application of deep learning, in particular, III. NEUROMORPHIC ARCHITECTURES AND deep neural network technologies such as convolutional NEUROCHIPS neural networks [4], autoencoders [5], recurrent LSTM networks [6] and the so on. However, these types of Presently existing spiking neural network architectures can networks are designed to solve specialized tasks (for be divided into the following two classes: digital using example, for face recognition or recognition of melodies), CMOS technologies and digital-analog based on memristors. require preliminary training and do not provide the so-called Digital architectures can be divided into the following types: incremental learning, or, in other words, the infinite "life- 1) Hard Architecture of Neural Nets (HAN) using time learning" which is necessary to create the intelligence of maximization of parallelism of working neurons and autonomous robots such as service robots. To provide this simplification of synapses (synapses do not have memory kind of learning, there is a field "growing neural networks" of weights), in neuroinformatics [7]. 2) Flexible Architecture of Neural Nets (FAN) with the In addition, in the last decade, work related to the hardware usage of memory for the parameters of synapses to learn implementation of neural networks [8], namely spiking neural network. (dynamic) neural networks (SNN) [9, 10, 11] has become 3) Growth Architecture of Neural Nets (GAN) with more active. The development and use of hardware static creation of new neurons and pruning during long term ANNs based on matrix arithmetic began back in the late activity of robot. 1980s, for example, on the basis of the RISC architecture at The HAN architecture uses a partitioning of multiple company Module [12], and is now continuing as NVIDIA neurons into multiple cores, within which a rapid exchange graphics accelerators [13]. But spiking neurochips not only of signals between neurons via the crossbar is provided. accelerate the work of neural networks, but also have low Between the cores, the interaction takes place according to a power consumption, which makes them attractive to use in simple protocol. Cores work in parallel and asynchronously autonomous intelligent robots. to minimize power consumption. Memory for storing the 400 978-1-5386-7054-5/18/$31.00 ©2018 IEEE 2018 14th International Scientific-Technical Conference APEIE – 44894 weights of synapses is not employed. The weight of the synapse is binary; means the absence or presence of a link between neurons. Training of the neural network takes place previously on the host computer in the form of training an ordinary convolutional network, usually with some specific limitations. The trained network is transformed into a network immersed in a neurochip when it is created. In this case, the weights of the synapses are converted into an excessive number of synapses and neurons, which replaces the storage of the values of the weights. Representatives of the HAN architecture are such neurochips as TrueNorth of IBM [15] (Fig. 1) and Altai of the company "Motive" [16] (Fig. 2). The main characteristics of the TrueNorth neurochip: - one million neurons combined into 4,096 nuclei, - 256 million synapses (256x256 per core), - about 400 megabytes of SRAM memory, - 5.4 billion transistors, - power consumption less than Fig. 2. Structure of neurochip “Altai”. 100 mW. The US Department of Defense plans to use the TrueNorth The flexible FAN architecture provides memory for storing neuro chip to detect and recognize ground targets [17]. At the the weights of synapses or other learning parameters of same time, it also successfully solves these problems as a neurons. Usually this memory is external to the neurochip. normal computer, but it spends 20 times less energy. This architecture has a neurochip developed within the framework of the SpiNNaker project [18]. Fig. 1. The board with neurochips TrueNorth. The domestic project "Altai" uses a similar architecture, some simplified "classical" model of spike neuron - LIF (Leaky-Integrate-and-Fire) with a constant leakage rate. By its characteristics of compactness, speed and energy efficiency, the neurochip "Altai" is slightly inferior to TrueNorth. According to the developers, the neurochip "Altai" can be used to solve the following problems [16]: • To analyze the video stream (in different spectral ranges): search, highlight and identify objects. • Building a 3D image of the surrounding space. • As a special calculator in a multi-touch navigation system. • To perform neural network "algorithms" for controlling robotic systems. Fig. 3. Usage of SpiNNaker chip for control of mobile robot. • For processing physiological signals in the human- machine interface. In [19] example of usage of SpiNNaker chip for control of • To detect equipment failures based on the analysis of robot is described. A SpiNNaker chip (octagon) is connected data streams on airborne traffic channels. to the robot and sensors using an interface board, responsible • For real-time processing of heterogeneous information of translating events into spikes and motor commands (Fig. from airborne sensors, units and assemblies. 3). The interface board is connected on one of the 6 asynchronous links connecting SpiNNaker chips together; each chip sees the robot and sensors as other SpiNNaker chips. 401 XIV Международная научно-техническая конференция АПЭП – 2018 This system was based on a 48-chip SpiNNaker board, for neural networks (deSNN) or dynamic developing spiking a total of 864 ARM968 Cores and 48 Gbit of memory, where neural networks. These networks are focused on on-line every chip has a top power consumption of 1W. This one training in real time and on recognition of space-time presented in Figure 1, is capable of simulating up to a quarter patterns. This approach is based on the concept of developing million neurons and more than 80 million synapses in real spiking neural networks, proposed earlier in [22] and to some time, within a power budget of less than 40W. Typically the extent similar to the theory of adaptive resonance for most expensive process in neural network simulations is the classical neural networks [23]. In this approach, the authors one concerning propagation and integration of spikes; with also use the Euclidean distance between the input vector and the current software infrastructure, the system used in this the cluster center in the algorithm that decides to create a paper is capable of delivering 1.8 billion synaptic events per new neuron (a new cluster) or not. In the latter case, the input second, using a few nJ per event and per neuron. pattern is recognized. The growing neuromorphic systems of the GAN The authors in deSNN use the encoding of information in architecture