Silicon Neurons That Compute

Choudhary, Swadesh; Sloan, Steven A.; Fok, Sam; Neckar, Alexander; Trautmann, Eric M.; Gao, Peiran; Stewart, Terry; Eliasmith, Chris; Boahen, Kwabena

doi:10.1007/978-3-642-33269-2_16

Cited by 72 publications

(46 citation statements)

References 8 publications

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“…It is therefore not unlikely that a future release of Neurogrid will implement synaptic plasticity. Currently, without any on-chip learning mechanisms available, Neurogrid can be used as an accelerator for reservoir computing or must be programmed with the Neural Engineering Framework (Choudhary et al, 2012).…”

Section: Learning With Neuromorphic Hardwarementioning

confidence: 99%

Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks

2015

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a b s t r a c tThe application of biologically inspired methods in design and control has a long tradition in robotics. Unlike previous approaches in this direction, the emerging field of neurorobotics not only mimics biological mechanisms at a relatively high level of abstraction but employs highly realistic simulations of actual biological nervous systems. Even today, carrying out these simulations efficiently at appropriate timescales is challenging. Neuromorphic chip designs specially tailored to this task therefore offer an interesting perspective for neurorobotics. Unlike Von Neumann CPUs, these chips cannot be simply programmed with a standard programming language. Like real brains, their functionality is determined by the structure of neural connectivity and synaptic efficacies. Enabling higher cognitive functions for neurorobotics consequently requires the application of neurobiological learning algorithms to adjust synaptic weights in a biologically plausible way. In this paper, we therefore investigate how to program neuromorphic chips by means of learning. First, we provide an overview over selected neuromorphic chip designs and analyze them in terms of neural computation, communication systems and software infrastructure. On the theoretical side, we review neurobiological learning techniques. Based on this overview, we then examine on-die implementations of these learning algorithms on the considered neuromorphic chips. A final discussion puts the findings of this work into context and highlights how neuromorphic hardware can potentially advance the field of autonomous robot systems. The paper thus gives an in-depth overview of neuromorphic implementations of basic mechanisms of synaptic plasticity which are required to realize advanced cognitive capabilities with spiking neural networks.

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Section: Learning With Neuromorphic Hardwarementioning

confidence: 99%

Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks

2015

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“…Each lookup provides a target's address as well as a weight that represents the probability that the spike is sent to this target [39]. In our experiment, all the weights were set to a probability of 1/16, which resulted in 4 outgoing spikes (to the mainboard) per incoming spike (to the daughterboard), on average.…”

Section: Router Powermentioning

confidence: 99%

A Multicast Tree Router for Multichip Neuromorphic Systems

Merolla

Arthur

Alvarez

et al. 2014

IEEE Trans. Circuits Syst. I

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Abstract-We present a tree router for multichip systems that guarantees deadlock-free multicast packet routing without dropping packets or restricting their length. Multicast routing is required to efficiently connect massively parallel systems' computational units when each unit is connected to thousands of others residing on multiple chips, which is the case in neuromorphic systems. Our tree router implements this one-tomany routing by branching recursively-broadcasting the packet within a specified subtree. Within this subtree, the packet is only accepted by chips that have been programmed to do so. This approach boosts throughput because memory look-ups are avoided enroute, and keeps the header compact because it only specifies the route to the subtree's root. Deadlock is avoided by routing in two phases-an upward phase and a downward phase-and by restricting branching to the downward phase. This design is the first fully implemented wormhole router with packet-branching that can never deadlock. The design's effectiveness is demonstrated in Neurogrid, a million-neuron neuromorphic system consisting of sixteen chips. Each chip has a 256×256 silicon-neuron array integrated with a full-custom asynchronous VLSI implementation of the router that delivers up to 1.17G words/s across the sixteen-chip network with less than 1µs jitter.

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“…Numerous implementations of multi-layered extreme learning machines have been developed [27][28][29], and it was found that the multi-layer implementation of extreme learning machine performed better than the conventional ELM in term of the recognition and classification performance. Recent works that were intended to develop neuromorphic implementations of Extreme Learning Machines [30][31][32] motivated this current work. Further details of neuromorphic implementations are described elsewhere [33].…”

Section: Introductionmentioning

confidence: 99%

Fabric Weave Pattern and Yarn Color Recognition and Classification Using a Deep ELM Network

et al. 2017

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Usually, a fabric weave pattern is recognized using methods which identify the warp floats and weft floats. Although these methods perform well for uniform or repetitive weave patterns, in the case of complex weave patterns, these methods become computationally complex and the classification error rates are comparatively higher. Furthermore, the fault-tolerance (invariance) and stability (selectivity) of the existing methods are still to be enhanced. We present a novel biologically-inspired method to invariantly recognize the fabric weave pattern (fabric texture) and yarn color from the color image input. We proposed a model in which the fabric weave pattern descriptor is based on the HMAX model for computer vision inspired by the hierarchy in the visual cortex, the color descriptor is based on the opponent color channel inspired by the classical opponent color theory of human vision, and the classification stage is composed of a multi-layer (deep) extreme learning machine. Since the weave pattern descriptor, yarn color descriptor, and the classification stage are all biologically inspired, we propose a method which is completely biologically plausible. The classification performance of the proposed algorithm indicates that the biologically-inspired computer-aided-vision models might provide accurate, fast, reliable and cost-effective solution to industrial automation.

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Silicon Neurons That Compute

Cited by 72 publications

References 8 publications

Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks

Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks

A Multicast Tree Router for Multichip Neuromorphic Systems

Fabric Weave Pattern and Yarn Color Recognition and Classification Using a Deep ELM Network

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