Real-time Closed Loop Neural Decoding on a Neuromorphic chip

Shaikh, Shoeb; So, Rosa; Sibindi, Tafadzwa; Libedinsky, Camilo; Basu, Arindam

doi:10.1109/ner.2019.8717122

“…13. A key difference to note is that SELMA was employed to build a regression model in this work as compared to a classification model reported in the earlier works [21], [48], [49]. During the testing phase, read back hidden layers (H) were multiplied by β as per Equation.…”

Section: B Experimental Setupmentioning

confidence: 99%

Towards Intelligent Intracortical BMI (i$^2$BMI): Low-Power Neuromorphic Decoders That Outperform Kalman Filters

Shaikh

¹

,

So

²

,

Sibindi

³

et al. 2019

IEEE Trans. Biomed. Circuits Syst.

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Fully implantable wireless intra-cortical Brain Machine Interfaces (iBMI) is one of the most promising next frontiers in the nascent field of neurotechnology. However, scaling the number of channels in such systems by another 10X is difficult due to power and bandwidth requirements of the wireless transmitter. One promising solution for that is to include more processing, up to the decoder, in the implant so that transmission data rate is reduced drastically. Earlier work on neuromorphic decoders only showed classification of discrete states. We present results for continuous state decoding using a low power neuromorphic decoder chip termed Spike-input Extreme Learning Machine (SELMA). We compared SELMA against state of the art Steady State Kalman Filter (SSKF) across two different datasets involving a total of 4 non-human primates (NHPs). Results show at least a 10% or more increase in the fraction of variance accounted for by SELMA over SSKF across the datasets. Furthermore, estimated energy consumption comparison shows SELMA consuming ≈ 9 nJ/update against SSKF's ≈ 7.4 nJ/update for an iBMI with a 10 degree of freedom control. Thus, SELMA yields better performance against SSKF with a marginal increase in energy consumption paving the way for reducing transmission data rates in future scaled BMI systems.

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“…12 to arrive at the decoded output Y. Although we are presenting offline results here, SELMA has been demonstrated to operate in a real-time closed loop setup involving a discrete-mode iBMI [49] and can easily be extended to continuous-mode iBMI as well. Fig.…”

Section: B Experimental Setupmentioning

confidence: 99%

Towards Intelligent Intra-cortical BMI (i²BMI): Low-power Neuromorphic Decoders that outperform Kalman Filters

Shaikh

¹

,

So

²

,

Sibindi

³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Fully implantable wireless intra-cortical Brain Machine Interfaces (iBMI) is one of the most promising next frontiers in the nascent field of neurotechnology. However, scaling the number of channels in such systems by another 10X is difficult due to power and bandwidth requirements of the wireless transmitter. One promising solution for that is to include more processing, up to the decoder, in the implant so that transmission data rate is reduced drastically. Earlier work on neuromorphic decoders only showed classification of discrete states. We present results for continuous state decoding using a low power neuromorphic decoder chip termed Spike-input Extreme Learning Machine (SELMA). We compared SELMA against state of the art Steady State Kalman Filter (SSKF) across two different datasets involving a total of 4 non-human primates (NHPs). Results show at least a 10% or more increase in the fraction of variance accounted for by SELMA over SSKF across the datasets. Furthermore, estimated energy consumption comparison shows SELMA consuming ≈ 9 nJ/update against SSKF's ≈ 7.4 nJ/update for an iBMI with a 10 degree of freedom control. Thus, SELMA yields better performance against SSKF with a marginal increase in energy consumption paving the way for reducing transmission data rates in future scaled BMI systems.

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“…The TrueNorth-compatible network achieved the highest classification, at approximately 76%. In [124], [125], the authors present a low-power neuromorphic platform named Spike-input Extreme Learning Machine [139], [140].…”

Section: B Towards Edge Processing For Biomedical Applications With Neuromorphic Processorsmentioning

confidence: 99%

Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications

Azghadi

¹

,

Lammie

²

,

Eshraghian

³

et al. 2020

IEEE Trans. Biomed. Circuits Syst.

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The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors has brought on new opportunities for applying both Deep and Spiking Neural Network (SNN) algorithms to healthcare and biomedical applications at the edge. This can facilitate the advancement of medical Internet of Things (IoT) systems and Point of Care (PoC) devices. In this paper, we provide a tutorial describing how various technologies including emerging memristive devices, Field Programmable Gate Arrays (FPGAs), and Complementary Metal Oxide Semiconductor (CMOS) can be used to develop efficient DL accelerators to solve a wide variety of diagnostic, pattern recognition, and signal processing problems in healthcare. Furthermore, we explore how spiking neuromorphic processors can complement their DL counterparts for processing biomedical signals. The tutorial is augmented with case studies of the vast literature on neural network and neuromorphic hardware as applied to the healthcare domain. We benchmark various hardware platforms by performing a sensor fusion signal processing task combining electromyography (EMG) signals with computer vision. Comparisons are made between dedicated neuromorphic processors and embedded AI accelerators in terms of inference latency and energy. Finally, we provide our analysis of the field and share a perspective on the advantages, disadvantages, challenges, and opportunities that various accelerators and neuromorphic processors introduce to healthcare and biomedical domains.

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Real-time Closed Loop Neural Decoding on a Neuromorphic chip

Cited by 11 publications

References 31 publications

Towards Intelligent Intracortical BMI (i$^2$BMI): Low-Power Neuromorphic Decoders That Outperform Kalman Filters

Towards Intelligent Intracortical BMI (i$^2$BMI): Low-Power Neuromorphic Decoders That Outperform Kalman Filters

Towards Intelligent Intra-cortical BMI (i²BMI): Low-power Neuromorphic Decoders that outperform Kalman Filters

Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications

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Real-time Closed Loop Neural Decoding on a Neuromorphic chip

Cited by 11 publications

References 31 publications

Towards Intelligent Intracortical BMI (i$^2$BMI): Low-Power Neuromorphic Decoders That Outperform Kalman Filters

Towards Intelligent Intracortical BMI (i$^2$BMI): Low-Power Neuromorphic Decoders That Outperform Kalman Filters

Towards Intelligent Intra-cortical BMI (i2BMI): Low-power Neuromorphic Decoders that outperform Kalman Filters

Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications

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Product

Resources

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Towards Intelligent Intra-cortical BMI (i²BMI): Low-power Neuromorphic Decoders that outperform Kalman Filters