Investigating Power Reduction for NoC-Based Spiking Neural Network Platforms using Channel Encoding

2012 ◽  
Vol 3 (4) ◽  
pp. 1-16
Author(s):  
Neil McDonnell ◽  
Snaider Carrillo ◽  
Jim Harkin ◽  
Liam McDaid
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Spiking Neural Network ◽  
Serial Links ◽  
Power Performance ◽  
Low Area ◽  
Embedded Platform ◽  
Data Links ◽  
Serial Data Links ◽  
Decision Circuit

Recent focus has been placed on exploring the possibility to switch from parallel to serial data links between NoC routers in order to improve signal integrity in the communication channel. However, moving streams of data between the parallel path of the internal router and external serial-channel links between them consumes additional power. One challenge is encoding the data and minimise the switching activity of data in the serial links in order to reduce the additional power dissipation; while under real-time and minimal hardware constraints. Consequently, proposed is a novel low area/power decision circuit for NoC channel encoding which identifies in real-time packets for encoding and extends the existing SILENT encoders/decoders to further minimise power consumption and demonstrates the power performance savings of the decision circuit and modified (en)decoders using example test traffic with the EMBRACE NoC router, a mixed signal spiking neural network (SNNs) embedded platform.

scholarly journals Control of a Humanoid NAO Robot by an Adaptive Bioinspired Cerebellar Module in 3D Motion Tasks

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Alberto Antonietti ◽  
Dario Martina ◽  
Claudia Casellato ◽  
Egidio D’Angelo ◽  
Alessandra Pedrocchi
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Upper Limb ◽  
Human Subjects ◽  
External Perturbation ◽  
Spiking Neural Network ◽  
Nao Robot ◽  
External Sources ◽  
Learning Behaviours ◽  
Humanoid Nao

A bioinspired adaptive model, developed by means of a spiking neural network made of thousands of artificial neurons, has been leveraged to control a humanoid NAO robot in real time. The learning properties of the system have been challenged in a classic cerebellum-driven paradigm, a perturbed upper limb reaching protocol. The neurophysiological principles used to develop the model succeeded in driving an adaptive motor control protocol with baseline, acquisition, and extinction phases. The spiking neural network model showed learning behaviours similar to the ones experimentally measured with human subjects in the same task in the acquisition phase, while resorted to other strategies in the extinction phase. The model processed in real-time external inputs, encoded as spikes, and the generated spiking activity of its output neurons was decoded, in order to provide the proper correction on the motor actuators. Three bidirectional long-term plasticity rules have been embedded for different connections and with different time scales. The plasticities shaped the firing activity of the output layer neurons of the network. In the perturbed upper limb reaching protocol, the neurorobot successfully learned how to compensate for the external perturbation generating an appropriate correction. Therefore, the spiking cerebellar model was able to reproduce in the robotic platform how biological systems deal with external sources of error, in both ideal and real (noisy) environments.

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’.

scholarly journals Design and validation of a real-time spiking-neural-network decoder for brain–machine interfaces

2013 ◽  
Vol 10 (3) ◽  
pp. 036008 ◽  
Author(s):  
Julie Dethier ◽  
Paul Nuyujukian ◽  
Stephen I Ryu ◽  
Krishna V Shenoy ◽  
Kwabena Boahen
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Spiking Neural Network ◽  
Brain Machine Interfaces

A spiking neural network for real-time Spanish vowel phonemes recognition

2017 ◽  
Vol 226 ◽  
pp. 249-261 ◽  
Author(s):  
L. Miró-Amarante ◽  
F. Gómez-Rodríguez ◽  
A. Jiménez-Fernández ◽  
G. Jiménez-Moreno
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Spiking Neural Network
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