Implementation of the Neural Engineering Framework on the TrueNorth Neurosynaptic System

Fischl, Kate D.; Andreou, Andreas G.; Stewart, Terrence C.; Fair, Kaitlin

doi:10.1109/biocas.2018.8584720

Cited by 12 publications

(9 citation statements)

References 17 publications

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“…It serves as the foundation for Nengo, a Python-based “neural compiler,” which translates high-level descriptions to low-level neural models (Bekolay et al, 2014 ). A version of NEF was compiled to work on the most prominent neuromorphic hardware architectures available, including the TrueNorth (Fischl et al, 2018 ), developed by IBM research, the Loihi (Lin et al, 2018 ), developed by Intel Labs, the NeuroGrid (Boahen, 2017 ), developed at Stanford University and the SpiNNaker (Mundy et al, 2015 ), developed at the University of Manchester.…”

Section: Introductionmentioning

confidence: 99%

Neuromorphic NEF-Based Inverse Kinematics and PID Control

et al. 2021

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Neuromorphic implementation of robotic control has been shown to outperform conventional control paradigms in terms of robustness to perturbations and adaptation to varying conditions. Two main ingredients of robotics are inverse kinematic and Proportional–Integral–Derivative (PID) control. Inverse kinematics is used to compute an appropriate state in a robot's configuration space, given a target position in task space. PID control applies responsive correction signals to a robot's actuators, allowing it to reach its target accurately. The Neural Engineering Framework (NEF) offers a theoretical framework for a neuromorphic encoding of mathematical constructs with spiking neurons for the implementation of functional large-scale neural networks. In this work, we developed NEF-based neuromorphic algorithms for inverse kinematics and PID control, which we used to manipulate 6 degrees of freedom robotic arm. We used online learning for inverse kinematics and signal integration and differentiation for PID, offering high performing and energy-efficient neuromorphic control. Algorithms were evaluated in simulation as well as on Intel's Loihi neuromorphic hardware.

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Section: Introductionmentioning

confidence: 99%

Neuromorphic NEF-Based Inverse Kinematics and PID Control

et al. 2021

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“…NEF is one of the most utilized theoretical frameworks in neuromorphic computing. A version of NEF was compiled on various neuromorphic digital systems, such as Intel’s Loihi and IBM’s TrueNorth ( Fischl et al, 2018 ; Lin et al, 2018 ), as well as on hybrid analog/digital systems such as the NeuroGrid ( Boahen, 2017 ). NEF-inspired neurons were directly implemented in both digital ( Wang et al, 2017 ) and analog ( Indiveri et al, 2011 ) circuitry.…”

Section: Discussionmentioning

confidence: 99%

“…It was compiled to work on multiple neuromorphic hardware using Nengo, a Python-based “neural compiler,” which translates high-level descriptions to low-level neural models ( Bekolay et al, 2014 ). NEF was shown to be incredibly versatile, as a version of it was compiled on each of the neuromorphic hardware designs listed earlier ( Mundy et al, 2015 ; Boahen, 2017 ; Fischl et al, 2018 ; Lin et al, 2018 ), although they do not follow the same paradigm of neuromorphic implementation. Although the Loihi, the TrueNorth, and the Spinnaker are pure digital systems, in the sense that both computing and communication are held digitally, the NeuroGrid is a mixed analog–digital circuit.…”

Section: Introductionmentioning

confidence: 99%

Neuromorphic Analog Implementation of Neural Engineering Framework-Inspired Spiking Neuron for High-Dimensional Representation

Hazan

Tsur

2021

Front. Neurosci.

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Brain-inspired hardware designs realize neural principles in electronics to provide high-performing, energy-efficient frameworks for artificial intelligence. The Neural Engineering Framework (NEF) brings forth a theoretical framework for representing high-dimensional mathematical constructs with spiking neurons to implement functional large-scale neural networks. Here, we present OZ, a programable analog implementation of NEF-inspired spiking neurons. OZ neurons can be dynamically programmed to feature varying high-dimensional response curves with positive and negative encoders for a neuromorphic distributed representation of normalized input data. Our hardware design demonstrates full correspondence with NEF across firing rates, encoding vectors, and intercepts. OZ neurons can be independently configured in real-time to allow efficient spanning of a representation space, thus using fewer neurons and therefore less power for neuromorphic data representation.

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“…The general techniques to map basic iterative linear algebra and dynamical systems based algorithms within the constraints of the TrueNorth can be used in a variety of other applications as well. Recently, work has been developed to implement the Neural Engineering Framework onto the TrueNorth architecture (Fischl et al, 2018), offering an abstraction for users to perform neural modeling on the hardware. The VMM was utilized as well as other concepts required for this work, such as positive and negative representations of a value, triggers, dynamic memory, and resets as utilized in the MUX component of this work.…”

Section: Discussionmentioning

confidence: 99%

Sparse Coding Using the Locally Competitive Algorithm on the TrueNorth Neurosynaptic System

et al. 2019

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The Locally Competitive Algorithm (LCA) is a biologically plausible computational architecture for sparse coding, where a signal is represented as a linear combination of elements from an over-complete dictionary. In this paper we map the LCA algorithm on the brain-inspired, IBM TrueNorth Neurosynaptic System. We discuss data structures and representation as well as the architecture of functional processing units that perform non-linear threshold, vector-matrix multiplication. We also present the design of the micro-architectural units that facilitate the implementation of dynamical based iterative algorithms. Experimental results with the LCA algorithm using the limited precision, fixed-point arithmetic on TrueNorth compare favorably with results using floating-point computations on a general purpose computer. The scaling of the LCA algorithm within the constraints of the TrueNorth is also discussed.

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Implementation of the Neural Engineering Framework on the TrueNorth Neurosynaptic System

Cited by 12 publications

References 17 publications

Neuromorphic NEF-Based Inverse Kinematics and PID Control

Neuromorphic NEF-Based Inverse Kinematics and PID Control

Neuromorphic Analog Implementation of Neural Engineering Framework-Inspired Spiking Neuron for High-Dimensional Representation

Sparse Coding Using the Locally Competitive Algorithm on the TrueNorth Neurosynaptic System

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