Corrigendum: Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain

Thakur, Chetan Singh; Molin, Jamal Lottier; Cauwenberghs, Gert; Indiveri, Giacomo; Kumar, Kundan; Qiao, Ning; Schemmel, Johannes; Wang, Runchun; Chicca, Elisabetta; Hasler, Jennifer; Seo, Jae-sun; Yu, Shimeng; Cao, Yu; Schaik, André van; Etienne‐Cummings, Ralph

doi:10.3389/fnins.2018.00991

Cited by 6 publications

(8 citation statements)

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“…EqSpike could also be sped up by building on dedicated hardware in analog or digital CMOS ( Schemmel et al, 2010 ; Qiao et al, 2015 ; Thakur et al, 2018 ; Frenkel et al, 2019 ; Park et al., 2020 ). Emerging nanotechnologies such as memristive synapses and nanoscale spiking oscillators are compelling candidates to scale up neuromorphic hardware due to their small size, their speed, and their low energy consumption ( Marković et al, 2020 ; Milo et al, 2020 ; Sebastian et al, 2020 ; Wang et al, 2020 ; Xi et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

“…Implementing the training of deep neural networks in such systems remains, however, a considerable challenge, as backpropagation does not apply directly to spiking networks and requires spatially non-local computations that go against the principles of neuromorphic systems. A large number of neuromorphic systems use the unsupervised and biologically inspired spike-timing-dependent plasticity (STDP) learning rule because its weight updates, based on the relative timing of pre- and post-synaptic spikes, are spatially local and can be achieved with compact circuits in several technologies ( Bi and Poo, 2001 ; Masquelier and Thorpe, 2007 ; Bichler et al, 2012 ; Zamarreño-Ramos et al, 2011 ; Jo et al, 2010 ; Pedretti et al, 2017 ; Serb et al, 2016 ; Prezioso et al, 2018 ; Thakur et al, 2018 ; Feldmann et al, 2019 ). Unfortunately, STDP weight updates generally do not minimize a global objective function for the network, and the accuracy of STDP-trained neural networks remains below state-of-the-art algorithms based on the error backpropagation ( Falez et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

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EqSpike: Spike-driven equilibrium propagation for neuromorphic implementations

et al. 2021

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Summary Finding spike-based learning algorithms that can be implemented within the local constraints of neuromorphic systems, while achieving high accuracy, remains a formidable challenge. Equilibrium propagation is a promising alternative to backpropagation as it only involves local computations, but hardware-oriented studies have so far focused on rate-based networks. In this work, we develop a spiking neural network algorithm called EqSpike, compatible with neuromorphic systems, which learns by equilibrium propagation. Through simulations, we obtain a test recognition accuracy of 97.6% on the MNIST handwritten digits dataset (Mixed National Institute of Standards and Technology), similar to rate-based equilibrium propagation, and comparing favorably to alternative learning techniques for spiking neural networks. We show that EqSpike implemented in silicon neuromorphic technology could reduce the energy consumption of inference and training, respectively, by three orders and two orders of magnitude compared to graphics processing units. Finally, we also show that during learning, EqSpike weight updates exhibit a form of spike-timing-dependent plasticity, highlighting a possible connection with biology.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EqSpike: Spike-driven equilibrium propagation for neuromorphic implementations

et al. 2021

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“…Inspired by biological sensory-processing systems, the neuromorphic sensing and processing paradigm targets these requirements by providing resilient, parallel, asynchronous, and highly distributed sensory-processing solutions (Mead, 1990 ; Liu and Delbruck, 2010 ; Hamilton et al, 2014 ). The resulting neuromorphic processors are non-Von Neumann computing architectures that feature local learning mechanisms and are capable, when combined with neuromorphic sensors, of time-continuous, asynchronous and distributed information processing, with high power efficiency than their conventional clock-based counterparts (Thakur et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Neuromorphic Engineering Needs Closed-Loop Benchmarks

Milde

Afshar

et al. 2022

Front. Neurosci.

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Neuromorphic engineering aims to build (autonomous) systems by mimicking biological systems. It is motivated by the observation that biological organisms—from algae to primates—excel in sensing their environment, reacting promptly to their perils and opportunities. Furthermore, they do so more resiliently than our most advanced machines, at a fraction of the power consumption. It follows that the performance of neuromorphic systems should be evaluated in terms of real-time operation, power consumption, and resiliency to real-world perturbations and noise using task-relevant evaluation metrics. Yet, following in the footsteps of conventional machine learning, most neuromorphic benchmarks rely on recorded datasets that foster sensing accuracy as the primary measure for performance. Sensing accuracy is but an arbitrary proxy for the actual system's goal—taking a good decision in a timely manner. Moreover, static datasets hinder our ability to study and compare closed-loop sensing and control strategies that are central to survival for biological organisms. This article makes the case for a renewed focus on closed-loop benchmarks involving real-world tasks. Such benchmarks will be crucial in developing and progressing neuromorphic Intelligence. The shift towards dynamic real-world benchmarking tasks should usher in richer, more resilient, and robust artificially intelligent systems in the future.

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“…12 ASIC design is another choice to perform the hardwired implementation of SNNs. [13][14][15][16] Pure analog, digital, and mixed-signal are three major approaches to this category. Memristor is a nanoscale and lowpower element which can be used as biological synapses in ASIC design.…”

Section: Introductionmentioning

confidence: 99%

“…Although GPUs are cost‐effective and provide speedup via parallel computing, they suffer from high power consumption and are also still far from appropriate parallelism for irregular applications such as SNNs 12 . ASIC design is another choice to perform the hardwired implementation of SNNs 13‐16 . Pure analog, digital, and mixed‐signal are three major approaches to this category.…”

Section: Introductionmentioning

confidence: 99%

A reconfigurable real‐time neuromorphic hardware for spiking winner‐take‐all network

Abdoli

Safari

2020

Circuit Theory & Apps

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The central nervous system receives a vast amount of sensory inputs, and it should be able to discriminate and recognize different kinds of multisensory information. Winner-take-all (WTA) consists of a simple recurrent neural network carrying out discrimination of input signals through competition. This paper presents a real-time scalable digital hardware implementation of the spiking WTA network. The need for concurrent computing, real-time performance, proper accuracy, and the reconfigurable device has led to the fieldprogrammable gate array (FPGA) as the target hardware platform. A set of techniques is employed to lessen memory and resource usage. The proposed architecture consists of multiprocessing elements, which share hardware resources between a specific number of neurons. We introduce a novel connectivity array for neurons (dedicated to the WTA network) to cut down memory usage. Also, a multiplier-less method in the neuron model and a novel tree adder in the synapse processing unit are designed to improve computational efficiency. The proposed network simulates 4,500 neurons in real time on a Xilinx Artix-7 FPGA, while a scalable architecture facilitates the implementation of up to 20,000 neurons on this device. The pipeline structure can guarantee real-time performance for large-scale networks. Based on simulation and physical synthesis results, the presented network mimics biological WTA dynamics and consumes efficient hardware resources.

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Corrigendum: Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain

Cited by 6 publications

References 0 publications

EqSpike: Spike-driven equilibrium propagation for neuromorphic implementations

EqSpike: Spike-driven equilibrium propagation for neuromorphic implementations

Neuromorphic Engineering Needs Closed-Loop Benchmarks

A reconfigurable real‐time neuromorphic hardware for spiking winner‐take‐all network

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