TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip

By F. Akopyan, J. Sawada, A. Cassidy, Rodrigo Alvarez-Icaza, John V. Arthur, Paul A. Merolla, Nabil Imam, Yutaka Y. Nakamura, Pallab Datta, Gi-Joon Nam and 8 more. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2015

1,575 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/681e086dbfe3860b57de18ce4f693b116a04c34e

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In Cellular and Molecular Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Learning to Detect Objects with a 1 Megapixel Event Camera (2020, methodology): “This could be done for example by adapting our method to run on neuromorphic hardware [56, 57].”
Event-Based Vision: A Survey (2019, background): “Processor SpiNNaker TrueNorth Loihi DYNAP Braindrop Reference [249] [250] [251] [252] [253]”
Demystifying Parallel and Distributed Deep Learning (2018, neutral): “mputational hardware is frequently proposed[225]. We see this trend with the NVIDIA Tensor Cores [185 ], the Tensor Processing Unit 124 , other ASICs and FPGAs 35,183 , and even neuromorphic computing[4]. Handling DNN sparsity (e.g., after compression) is a focus for some ASICs [259 ], and advances ”
A Multitask Grocery Assist System for the Visually Impaired: Smart glasses, gloves, and shopping carts provide auditory and tactile feedback (2017, methodology): “A key advantage of this chip is that it consumes merely 65 mW of power while running a typical computer-vision application [12].”
A 0.086-mm$^2$ 12.7-pJ/SOP 64k-Synapse 256-Neuron Online-Learning Digital Spiking Neuromorphic Processor in 28-nm CMOS (2018, background): “However, in order to fully leverage technology scaling, several research groups recently started designing digital SNNs (e.g., Seo et al. in [28], Kim et al. in [29], IBM with TrueNorth [30] and Intel with Loihi [31]).”
Survey of Machine Learning Accelerators (2020, methodology): “• The TrueNorth 〈TrueNorth〉 [16], [43], [44] is a digital neuromorphic research chip from the IBM Almaden research lab.”
Memory-Efficient Deep Learning on a SpiNNaker 2 Prototype (2018, background): “, 2017) and power consumption (Schemmel et al., 2008; Akopyan et al., 2015) in special purpose hardware and the required memory and computation time (Brette et al.”
Hebbian Learning based Orthogonal Projection for Continual Learning of Spiking Neural Networks (2024, methodology): “…systems imitate biological neurons with parallel in-memory and event-driven neuronal computation and can be efficiently deployed on neuromorphic hardware for energy-efficient applications (Akopyan et al., 2015; Davies et al., 2018; Pei et al., 2019; Wo´zniak et al., 2020; Rao et al., 2022).”