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

Jiménez-Fernández, Ángel; Cerezuela-Escudero, Elena; Miró-Amarante, Lourdes; Dominguez-Moralse, Manuel Jesus; Gómez-Rodríguez, F.; Linares-Barranco, A.; Jiménez-Moreno, Gabriel

doi:10.1109/tnnls.2016.2583223

Cited by 79 publications

(86 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…FP-DNN [51] and the CNN RTL Compiler [52] proposed RTL-level optimisations in order to reach high performance and were the first works to target residual networks. Moreover, [53] and [54] presented automated frameworks specifically tailored for FPGA-based binarised and spiking neural networks respectively, while [55] proposed a library for the mapping of ConvNets on diverse embedded platforms, together with a comparative study of their design spaces. Finally, [12] provides a detailed survey of ConvNet-to-FPGA toolflows.…”

Section: Related Workmentioning

confidence: 99%

fpgaConvNet: Mapping Regular and Irregular Convolutional Neural Networks on FPGAs

Venieris

Bouganis

2019

IEEE Trans. Neural Netw. Learning Syst.

121

View full text Add to dashboard Cite

Since neural networks renaissance, convolutional neural networks (ConvNets) have demonstrated a state-of-the-art performance in several emerging artificial intelligence tasks. The deployment of ConvNets in real-life applications requires power-efficient designs that meet the application-level performance needs. In this context, field-programmable gate arrays (FPGAs) can provide a potential platform that can be tailored to application-specific requirements. However, with the complexity of ConvNet models increasing rapidly, the ConvNet-to-FPGA design space becomes prohibitively large. This paper presents fpgaConvNet, an end-to-end framework for the optimized mapping of ConvNets on FPGAs. The proposed framework comprises an automated design methodology based on the synchronous dataflow (SDF) paradigm and defines a set of SDF transformations in order to efficiently navigate the architectural design space. By proposing a systematic multiobjective optimization formulation, the presented framework is able to generate hardware designs that are cooptimized for the ConvNet workload, the target device, and the application's performance metric of interest. Quantitative evaluation shows that the proposed methodology yields hardware designs that improve the performance by up to 6.65$x$ over highly optimized graphics processing unit designs for the same power constraints and achieve up to 2.94$x$ higher performance density compared with the state-of-the-art FPGA-based ConvNet architectures.

show abstract

Section: Related Workmentioning

confidence: 99%

fpgaConvNet: Mapping Regular and Irregular Convolutional Neural Networks on FPGAs

Venieris

Bouganis

2019

IEEE Trans. Neural Netw. Learning Syst.

121

View full text Add to dashboard Cite

show abstract

“…The fact that the output frequency depends on the time 332 constant of feedback inhibition means that correspondence between output and center frequencies could be 333 facilitated by matching interneuron types in the converter and filter networks. Furthermore, the 334 variable-frequency response could potentially serve encoding of slowly-varying asynchronous signals using 335 pulse-frequency modulation [39] or participation in a phase-locked loop. Such systems would need to account for, 336 or be invariant to, the concurrent amplitude modulation of the PC/IN network.…”

mentioning

confidence: 99%

Flexible resonance in prefrontal networks with strong feedback inhibition

Sherfey

Ardid

Haß

et al. 2018

Preprint

View full text Add to dashboard Cite

Oscillations are ubiquitous features of brain dynamics that undergo task-related changes in synchrony, power, and frequency. The impact of those changes on target networks is poorly understood. In this work, we used a biophysically detailed model of prefrontal cortex (PFC) to explore the effects of varying the spike rate, synchrony, and waveform of strong oscillatory inputs on the behavior of cortical networks driven by them. Interacting populations of excitatory and inhibitory neurons with strong feedback inhibition are inhibition-based network oscillators that exhibit resonance (i.e., larger responses to preferred input frequencies). We quantified network responses in terms of mean firing rates and the population frequency of network oscillation; and characterized their behavior in terms of the natural response to asynchronous input and the resonant response to oscillatory inputs. We show that strong feedback inhibition causes the PFC to generate internal (natural) oscillations in the beta/gamma frequency range (>15 Hz) and to maximize principal cell spiking in response to external oscillations at slightly higher frequencies. Importantly, we found that the fastest oscillation frequency that can be relayed by the network maximizes local inhibition and is equal to a frequency even higher than that which maximizes the firing rate of excitatory cells; we call this phenomenon population frequency resonance. This form of resonance is shown to determine the optimal driving frequency for suppressing responses to asynchronous activity. Lastly, we demonstrate that the natural and resonant frequencies can be tuned by changes in neuronal excitability, the duration of feedback inhibition, and dynamic properties of the input. Our results predict that PFC networks are tuned for generating and selectively responding to beta-and gamma-rhythmic signals due to the natural and resonant properties of inhibition-based oscillators. They also suggest strategies for optimizing transcranial stimulation and using oscillatory networks in neuromorphic engineering.Keywords: prefrontal cortex; network oscillations; biophysically-detailed model; beta rhythm; frequency-based gating; frequency response Author Contributions: J.S. designed the study, performed the simulations, and wrote the paper. S. A., J. H., and N. K. provided extensive feedback through many discussions of the results. N. K. helped write the paper. M. H., S. A., and J. H. reviewed the paper. CC-BY-NC-ND 4.0 International licenseIt is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint (which . http://dx.doi.org/10.1101/364729 doi: bioRxiv preprint first posted online Jul. 7, 2018; 2 Author SummaryThe prefrontal cortex (PFC) flexibly encodes task-relevant representations and outputs biases to mediate higher cognitive functions. The relevant neural ensembles undergo task-related changes in oscillatory dynamics at betaand gamma frequencies. Us...

show abstract

“…As a future work, we propose to use some voice commands and a neuromorphic auditory sensor [27] to change the gait. This real-time change will be available based on the classification achieved by an SNN connected to the sCPG.…”

Section: Discussionmentioning

confidence: 99%

Neuropod: A real-time neuromorphic spiking CPG applied to robotics

et al. 2020

Self Cite

View full text Add to dashboard Cite

Initially, robots were developed with the aim of making our life easier, carrying out repetitive or dangerous tasks for humans. Although they were able to perform these tasks, the latest generation of robots are being designed to take a step further, by performing more complex tasks that have been carried out by smart animals or humans up to date. To this end, inspiration needs to be taken from biological examples. For instance, insects are able to optimally solve complex environment navigation problems, and many researchers have started to mimic how these insects behave. Recent interest in neuromorphic engineering has motivated us to present a real-time, neuromorphic, spike-based Central Pattern Generator of application in neurorobotics, using an arthropod-like robot. A Spiking Neural Network was designed and implemented on SpiNNaker. The network models a complex, online-change capable Central Pattern Generator which generates three gaits for a hexapod robot locomotion. Reconfigurable hardware was used to manage both the motors of the robot and the real-time communication interface with the Spiking Neural Networks. Real-time measurements confirm the simulation results, and locomotion tests show that NeuroPod can perform the gaits without any balance loss or added delay.

show abstract

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

Cited by 79 publications

References 39 publications

fpgaConvNet: Mapping Regular and Irregular Convolutional Neural Networks on FPGAs

fpgaConvNet: Mapping Regular and Irregular Convolutional Neural Networks on FPGAs

Flexible resonance in prefrontal networks with strong feedback inhibition

Neuropod: A real-time neuromorphic spiking CPG applied to robotics

Contact Info

Product

Resources

About