Sequential and parallel real-time simulation of a flexible manipulator system

Robotica ◽  
1998 ◽  
Vol 16 (4) ◽  
pp. 445-456
Author(s):  
M. O. Tokhi ◽  
M. A. Hossain ◽  
A. K. M. Azad
Keyword(s):  
Real Time ◽  
Finite Difference Approximation ◽  
Flexible Manipulator ◽  
Difference Approximation ◽  
Real Time Processing ◽  
Real Time Simulation ◽  
Time Simulation ◽  
Dimensional Simulation ◽  
Comparison Of The Results ◽  
And Control

This paper presents an investigation into the utilisation of sequential and parallel processing techniques for the real-time simulation of a flexible manipulator system. A finite dimensional simulation of the system is developed using a finite difference approximation to the governing dynamic equation of the manipulator. The developed algorithm is implemented on a number of uni-processor and multi-processor, homogeneous and heterogeneous, parallel architectures. A comparison of the results of these implementations is made and discussed, on the basis of real-time processing requirements in the simulation and control of flexible manipulator systems.

scholarly journals Real-time simulation of a cat-scale artificial cerebellum on PEZY-SC processors

2017 ◽  
Vol 33 (1) ◽  
pp. 155-168 ◽  
Author(s):  
Tadashi Yamazaki ◽  
Jun Igarashi ◽  
Junichiro Makino ◽  
Toshikazu Ebisuzaki
Keyword(s):  
Real Time ◽  
Peak Performance ◽  
Physiological Data ◽  
Real Time Simulation ◽  
Time Simulation ◽  
Spiking Network ◽  
Effective Performance ◽  
Motor Learning And Control ◽  
Time Motor ◽  
And Control

The cerebellum is a part of the brain that plays essential roles in real-time motor learning and control and even cognitive functions. Thanks to the large amount of anatomical and physiological data, we implemented a spiking network model of the cerebellum on Shoubu, an energy-efficient supercomputer with 1280 PEZY-SC processors at RIKEN. Our artificial cerebellum consists of more than one billion neurons, which is comparable to the whole cerebellum of a cat. Using 1008 of 1280 processors on Shoubu, we achieved real-time simulation, that is, a computer simulation of the model for 1 s completes within 1 s of the real world with temporal resolution of 1 ms. Effective performance was estimated as 68 teraflops in single-precision floating points, suggesting 2.6% of peak performance. We expect that the artificial cerebellum will be applicable to various engineering applications, such as robotics and brain-style artificial intelligence.

scholarly journals Real-time cortical simulation on neuromorphic hardware

2019 ◽  
Vol 378 (2164) ◽  
pp. 20190160 ◽  
Author(s):  
Oliver Rhodes ◽  
Luca Peres ◽  
Andrew G. D. Rowley ◽  
Andrew Gait ◽  
Luis A. Plana ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Large Scale ◽  
Spiking Neural Network ◽  
Real Time Processing ◽  
Real Time Simulation ◽  
Statistical Measures ◽  
Time Simulation ◽  
Neuromorphic Hardware ◽  
Cortical Microcircuit

Real-time simulation of a large-scale biologically representative spiking neural network is presented, through the use of a heterogeneous parallelization scheme and SpiNNaker neuromorphic hardware. A published cortical microcircuit model is used as a benchmark test case, representing ≈1 mm 2 of early sensory cortex, containing 77 k neurons and 0.3 billion synapses. This is the first hard real-time simulation of this model, with 10 s of biological simulation time executed in 10 s wall-clock time. This surpasses best-published efforts on HPC neural simulators (3 × slowdown) and GPUs running optimized spiking neural network (SNN) libraries (2 × slowdown). Furthermore, the presented approach indicates that real-time processing can be maintained with increasing SNN size, breaking the communication barrier incurred by traditional computing machinery. Model results are compared to an established HPC simulator baseline to verify simulation correctness, comparing well across a range of statistical measures. Energy to solution and energy per synaptic event are also reported, demonstrating that the relatively low-tech SpiNNaker processors achieve a 10 × reduction in energy relative to modern HPC systems, and comparable energy consumption to modern GPUs. Finally, system robustness is demonstrated through multiple 12 h simulations of the cortical microcircuit, each simulating 12 h of biological time, and demonstrating the potential of neuromorphic hardware as a neuroscience research tool for studying complex spiking neural networks over extended time periods. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’.

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