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On Usage of Neuromorphic Engineering in Autonomous Robots

On Usage of Neuromorphic Engineering in Autonomous Robots

XIV Международная научно-техническая конференция АПЭП – 2018

On Usage of Neuromorphic Engineering in Autonomous

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 . 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 . 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 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 system based on hardware spiking any problems, like a . 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 , in particular, III. NEUROMORPHIC ARCHITECTURES AND deep neural network technologies such as convolutional NEUROCHIPS neural networks [4], [5], recurrent LSTM networks [6] and the so on. However, these types of Presently existing 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 . 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 [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 , - 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.

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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 are the new direction [7] that is emerging, which the form of the order of occurrence of spikes (rank-order does not yet exist in the form of neurochips. Given the learning) and the algorithm SDSP (Spike Driven Synaptic existing level of technology, hardware support of this type of Plasticity) - the variant of STDP (Spike-Timing-Dependent neuromorphic systems is possible in the form of a Plasticity [24]). At the same time, they used three versions of combination of a parallel neural network like TrueNorth with the training modes: without a teacher (unsupervised), with a some hardware redundancy to enable or disable new neurons teacher (supervised) and semi-supervised. The SDSP or to simulate parallelism when the number of neurons algorithm was used to dynamically change the link weights exceeds a certain limit specified by the neurochip's for locating space-time patterns in both training and using a capabilities. Below are given some examples of approaches trained network. The proposed deSNN model was tested for to constructing growing spiking neural networks. two tasks: 1) moving object recognition, and 2) recognition Most known approach to building of growth spiking neural of the electroencephalogram in the brain-computer networks is based on genetic algorithms (GA) [20]. The interaction system. genome in this approach contains not only the parameters of The basis of promising analog-digital neuromorphic synapses and neurons, but also the topology of the network. systems is memristors (memory resistor) - bipolar devices In experiments, the trained pulsed neural network whose electrical resistance reversibly changes from a high- demonstrated generation of an output pulse pattern (for resistance state (HRS) to a low-resistance state (LRS), example, for controlling the gait of a mobile robot) in depending on the current flowing through it. By reducing the response to an input sequence and interference resistance in resistance of the in the nodes of the neural an input pattern. Other tasks that were solved with this network, a connection is formed, the stability and weight of approach: detection of a given input sequence, detection of which depend on the properties of the memristor. When the distortion of the input pattern. The topology of the neural resistance of the memristor increases, forgetting or slowing network was described by a bond matrix, which was encoded down occurs. An important feature of memristor memory is using a special algorithm for placement in the chromosome. its non-volatility. storage of information is carried out not by In addition to the topology in the chromosome, the following storing the charge, but by changing the crystal lattice of the parameters were placed for each neuron: the refractory material. period, the time constant specifying the steepness of the An attractive features of memristor are non-volatility and exponent of the return of the membrane potential to the directly implementation of STDP (Spike-Timing-Dependent initial value. In addition, the following parameters of each Plasticity. synapse were coded in the chromosome: weight, delay, time The first memristors were obtained in 2008 [25] based on constants, determining exponential growth and reduction of titanium dioxide, although theoretically the idea of a post-synaptic potential. A similar approach, based on the use memristor was proposed back in 1971 [26]. Now most of of genetic algorithms, was proposed in [21]. Here, fewer known application of memristor is permanent memory neuron parameters are used to train the network and the developed by HP. As for neuromorphic systems usage of classification problem is solved. The algorithms proposed by memristors is still at the initial stage. the authors were tested on the problems of classification of irises, the diagnosis of breast cancer and hepatitis. The disadvantage of using genetic algorithms for learning IV. HYBRID ARCHITECTURE OF BRAIN FOR of spiking neural networks is that it takes a lot of time to AUTONOMOUS ROBOTS learn. The advantage is that virtually any parameters of The functions of the robot control system can be divided neurons, synapses and the network as a whole can be used into energy-intensive and low-power, and also those for training. So the GA based approach to growth SNN may requiring continuous training and not requiring it (in this be used in robotics only for previous training of NN before case, we can limit ourselves to preliminary training at the loading of its topology into chip. stage of creating the robot). Autonomous robots can be In the article [22] the authors proposed another approach divided into the following types from the point of view of the for constructing growing pulsed neural networks without the potential effectiveness of the application of neurochips: use of genetic algorithms. The authors proposed a new class 1) focused on the use of simple sensors that do not require of spiking neural networks - dynamic evolving spiking image processing or ,

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2018 14th International Scientific-Technical Conference APEIE – 44894

2) focused on the use of video information and / or speech with limited capability to of SNN). i.e. recognition, working in “reactive paradigm” [35]. 3) oriented to work in a previously known and/or simplified external environment, 4) oriented to work in a previously unknown environment of a real level of complexity. V. CONCLUSION Static neural networks have long been used in robots of the 1st type [27, 28, 29], in particular, growth neural networks In this article different problems and approaches on usage providing long-term incremental learning [30, 31]. Also of hardware spiking neural networks for autonomous publications on implementation of robotic control systems robotics are discussed. Study of state of art in development based on neuromorphic chips [32, 33]. of neurochips leads to follow statements: Obviously, a hard neuromorphic architecture HAN can be - most well developed and known neurochips have one most effective for the 1st and 3rd types of autonomous of two disadvantages: robots. The above example of the application of the o sufficient limits to long-term (or life-long) TrueNorth neurochip relates to robots of the third type, learn needed for autonomous robots (such as because the neurochip is trained in advance to recognize a TrueNorth) keeping low energy consumption limited number of known terrestrial objects. or In order to take advantage of all the advantages of o ore energy consumption with capability to neuromorphic chips, taking into account their shortcomings life-long learning because of external RAM at the current level of technologies, it is proposed to use a to store parameters of neural network hybrid approach in constructing an intelligent robot control (synaptic weights and topology of net); system [34]. - neurochips based on memristors with nonvolatile Hybrid architecture of autonomous robot control system memory for synaptic weights may be developed hardware consists of (Fig. 4): through no less than 10 years; - usual CPU based subsystem for decision making by - to develop growth neural network in neurochip today logic inference and natural language processing for seems impossible, growth neural networks may be interaction with people; only simulated using parallel processing of neurons by - some subsystems based on neurochips for previous any CPU; processing of signals from different sensors, visual - today it is possible to use hybrid approach combining image processing and recognition, speech recognition, neurochips with usual CPU based computer; audio patterns recognition, faults and collisions - to decrease energy consumption CPU based subsystem recognition, generation of signal sequences for motors. can work only when it is needed (for example, during planning or interaction with human), in another time CPU based SNN for generation robots may be controlled by trained hardware neural subsystem of speech networks (in reactive paradigm). 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