Towards AER VITE: building spike gate signal

Neuromorphic engineers aim to mimic the precise and efficient mechanisms of the nervous system to process information using spikes from sensors to actuators. There are many available works that sense and process information in a spike-based way. But there are still several gaps in the actuation and motor control field in a spike-based way. Spike-based Proportional-Integrative-Derivative controllers (PID) are present in the literature. On the other hand, neuro-inspired control models as VITE (Vector Integration To End point) and FLETE (Factorization of muscle Length and muscle Tension) are also present in the literature. This paper presents another step toward the spike implementation of those neuro-inspired models. We present a spike-based ramp multiplier. VITE algorithm generates the way to achieve a final position targeted by a mobile robotic arm. The block presented is used as a gate for the way involved and it also puts the incoming movement on speed with a variable slope profile. Only spikes for information representation were used and the process is in real time. The software simulation based on Simulink and Xilinx System Generator shows the accurate adjust to the traditional processing for short time periods and the hardware tests confirm and extend the previous simulated results for any time. We have implemented the spikes generator, the ramp multiplier and the low pass filter into the Virtex-5 FPGA and connected this with an USB-AER (Address Event Representation) board to monitor the spikes.Ministerio de Ciencia e Innovación TEC2009-10639-C04-0

Passing from a gas to an electric water heater system: adaptive PID versus Smith predictive control

“Copyright © [2007] IEEE. Reprinted from 11th International Conference on Intelligent Engineering Systems , 2007. ISBN:1-4244-1147-5 This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.”This paper presents the control results of an electric water heater system using two approaches: adaptable proportional integral derivative and Smith predictive control based in the physical internal model control structure. The electric water heater was modelled with two variable blocks connected in series: a first order system and a time delay. In fact, the gain, the time constant and the time delay of the system change linearly with the water that flows in the permutation chamber. The physical model of the electric water heater system was retched based in energy dynamic equations and validated with open loop data of the system in a similar way that was made in a previews study about modelling and controlling a gas water heater. The two different control algorithms explored are the adaptive proportional integral derivative (APID) and the Smith predictive control (SPC) based in the internal physical model control algorithm. The first approach has some problems dealing with the time constant and the time delay variations of the system. This solution can control the overshoot for all different water flows but the time constant of the close loop systems changes with the water flow. The APID does not deal well with water flow variations. The second approach is more adequate to control this kind of systems (first order system followed by a time delay that changes in time). The SPC loop is indicated for control time delay systems and with the à priori knowledge of the physical model we can achieve a very good control result. Finally, these two algorithms are applied in controlling the system and the results are compared using the mean square error criterion

A Binaural Neuromorphic Auditory Sensor for FPGA: A Spike Signal Processing Approach

This paper presents a new architecture, design flow, and field-programmable gate array (FPGA) implementation analysis of a neuromorphic binaural auditory sensor, designed completely in the spike domain. Unlike digital cochleae that decompose audio signals using classical digital signal processing techniques, the model presented in this paper processes information directly encoded as spikes using pulse frequency modulation and provides a set of frequency-decomposed audio information using an address-event representation interface. In this case, a systematic approach to design led to a generic process for building, tuning, and implementing audio frequency decomposers with different features, facilitating synthesis with custom features. This allows researchers to implement their own parameterized neuromorphic auditory systems in a low-cost FPGA in order to study the audio processing and learning activity that takes place in the brain. In this paper, we present a 64-channel binaural neuromorphic auditory system implemented in a Virtex-5 FPGA using a commercial development board. The system was excited with a diverse set of audio signals in order to analyze its response and characterize its features. The neuromorphic auditory system response times and frequencies are reported. The experimental results of the proposed system implementation with 64-channel stereo are: a frequency range between 9.6 Hz and 14.6 kHz (adjustable), a maximum output event rate of 2.19 Mevents/s, a power consumption of 29.7 mW, the slices requirements of 11 141, and a system clock frequency of 27 MHz.Ministerio de Economía y Competitividad TEC2012-37868-C04-02Junta de Andalucía P12-TIC-130

Implementation of bioinspired algorithms on the neuromorphic VLSI system SpiNNaker 2

It is believed that neuromorphic hardware will accelerate neuroscience research and enable the next generation edge AI. On the other hand, brain-inspired algorithms are supposed to work efficiently on neuromorphic hardware. But both processes don't happen automatically. To efficiently bring together hardware and algorithm, optimizations are necessary based on the understanding of both sides. In this work, software frameworks and optimizations for efficient implementation of neural network-based algorithms on SpiNNaker 2 are proposed, resulting in optimized power consumption, memory footprint and computation time. In particular, first, a software framework including power management strategies is proposed to apply dynamic voltage and frequency scaling (DVFS) to the simulation of spiking neural networks, which is also the first-ever software framework running a neural network on SpiNNaker 2. The result shows the power consumption is reduced by 60.7% in the synfire chain benchmark. Second, numerical optimizations and data structure optimizations lead to an efficient implementation of reward-based synaptic sampling, which is one of the most complex plasticity algorithms ever implemented on neuromorphic hardware. The results show a reduction of computation time by a factor of 2 and energy consumption by 62%. Third, software optimizations are proposed which effectively exploit the efficiency of the multiply-accumulate array and the flexibility of the ARM core, which results in, when compared with Loihi, 3 times faster inference speed and 5 times lower energy consumption in a keyword spotting benchmark, and faster inference speed and lower energy consumption for adaptive control benchmark in high dimensional cases. The results of this work demonstrate the potential of SpiNNaker 2, explore its range of applications and also provide feedback for the design of the next generation neuromorphic hardware

MPPT control of PV array based on PSO and adaptive controller

In general, photovoltaic (PV) array is not able to generate maximum power automatically, because some partial shading caused by trees, clouds, or buildings. Irradiation imperfections received by the PV array are overcome by applying maximum power point tracking (MPPT) to the output of the PV array. In order to overcome these partial shading problems, this system is employing particle swarm optimization (PSO) as MPPT method. It optimizes the output power of the solar PV array by Zeta converter. Output voltage of MPPT has high rate such that it needs stepdown device to regulate certain voltage. Constant voltage will be the input voltage of buck converter and controlled using adaptive PID. Adaptive control based on model reference adaptive control (MRAC) has design that almost same as the conventional PID structure and it has better performance in several conditions. The proposed system is expected to have stable output and able to perfectly emulate the response of the reference model. From the simulation results, it appears that PSO have high tracking accuracy and high tracking speed to reach maximum power of PV array. In the output voltage regulation, adaptive control does not have a stable error status and consistently follows the set point value

Hybrid optimization techniques based automatic artificial respiration system for corona patient

Artificial ventilation is widely used for various respiratory problems of human beings. The oxygen level of the corona patients has to be maintained for smooth breathing which is very difficult. For achieving this state, the air pressure should be controlled in the respiration system that has a piston mechanism driven by a motor. An Automatic respiration system model is designed and controller parameters are tuned using hybrid Optimization techniques. Hybrid Controllers like genetic algorithm based Fractional Order Proportional Integral Derivative controller (FOPID), Fmincon-Pattern search Algorithm based Proportional Integral Derivative (PID) controller, and Hybrid Model predictive control (MPC) – Proportional Integral Derivative (PID) controllers were designed and verified. Integral Square Error is considered as the objective function of the optimization technique to find the controller parameters. The output responses of all three hybrid controllers are compared based on the error indices, time domain specifications, set-point tracking and Convergence speed graph. The genetic algorithm-based FOPID controller gives better results when compared with the Fmincon-Pattern search Algorithm based Proportional Integral Derivative (PID) controller and Hybrid Model predictive control (MPC) – Proportional Integral Derivative (PID) for the proposed artificial ventilation system

A Survey on FPGA-Based Sensor Systems: Towards Intelligent and Reconfigurable Low-Power Sensors for Computer Vision, Control and Signal Processing

The current trend in the evolution of sensor systems seeks ways to provide more accuracy and resolution, while at the same time decreasing the size and power consumption. The use of Field Programmable Gate Arrays (FPGAs) provides specific reprogrammable hardware technology that can be properly exploited to obtain a reconfigurable sensor system. This adaptation capability enables the implementation of complex applications using the partial reconfigurability at a very low-power consumption. For highly demanding tasks FPGAs have been favored due to the high efficiency provided by their architectural flexibility (parallelism, on-chip memory, etc.), reconfigurability and superb performance in the development of algorithms. FPGAs have improved the performance of sensor systems and have triggered a clear increase in their use in new fields of application. A new generation of smarter, reconfigurable and lower power consumption sensors is being developed in Spain based on FPGAs. In this paper, a review of these developments is presented, describing as well the FPGA technologies employed by the different research groups and providing an overview of future research within this field.The research leading to these results has received funding from the Spanish Government and European FEDER funds (DPI2012-32390), the Valencia Regional Government (PROMETEO/2013/085) and the University of Alicante (GRE12-17)

Embodied neuromorphic intelligence

The design of robots that interact autonomously with the environment and exhibit complex behaviours is an open challenge that can benefit from understanding what makes living beings fit to act in the world. Neuromorphic engineering studies neural computational principles to develop technologies that can provide a computing substrate for building compact and low-power processing systems. We discuss why endowing robots with neuromorphic technologies – from perception to motor control – represents a promising approach for the creation of robots which can seamlessly integrate in society. We present initial attempts in this direction, highlight open challenges, and propose actions required to overcome current limitations

Enhanced Temperature Control Method Using ANFIS with FPGA

Temperature control in etching process is important for semiconductor manufacturing technology. However, pressure variations in vacuum chamber results in a change in temperature, worsening the accuracy of the temperature of the wafer and the speed and quality of the etching process. This work develops an adaptive network-based fuzzy inference system (ANFIS) using a field-programmable gate array (FPGA) to improve the effectiveness. The proposed method adjusts every membership function to keep the temperature in the chamber stable. The improvement of the proposed algorithm is confirmed using a medium vacuum (MV) inductively-coupled plasma- (ICP-) type etcher

Implementation of Adaptive Critic-Based Neurocontrollers for Turbogenerators in a Multimachine Power System

This paper presents the design and practical hardware implementation of optimal neurocontrollers that replace the conventional automatic voltage regulator (AVR) and the turbine governor of turbogenerators on multimachine power systems. The neurocontroller design uses a powerful technique of the adaptive critic design (ACD) family called dual heuristic programming (DHP). The DHP neurocontroller\u27s training and testing are implemented on the Innovative Integration M67 card consisting of the TMS320C6701 processor. The measured results show that the DHP neurocontrollers are robust and their performance does not degrade unlike the conventional controllers even when a power system stabilizer (PSS) is included, for changes in system operating conditions and configurations. This paper also shows that it is possible to design and implement optimal neurocontrollers for multiple turbogenerators in real time, without having to do continually online training of the neural networks, thus avoiding risks of instability