A million spiking-neuron integrated circuit with a scalable communication network and interface

By Paul A. Merolla, John V. Arthur, Rodrigo Alvarez-Icaza, A. Cassidy, J. Sawada, F. Akopyan, Bryan L. Jackson, Nabil Imam, Chen Guo, Yutaka Y. Nakamura and 10 more. 2014

3,922 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

1. Michael Pfeiffer
2. Kaushik Roy
3. Timothée Masquelier
4. Shih-Chii Liu
5. Shimeng Yu
6. Yonghong Tian
7. Garrick Orchard
8. H. Philip Wong

In Cognitive Neuroscience

1. Kaushik Roy
2. Timothée Masquelier
3. Gopalakrishnan Srinivasan
4. Michael Pfeiffer
5. Yonghong Tian
6. Shih-Chii Liu
7. Guoqi Li
8. Thomas Pfeil

In Electrical and Electronic Engineering

1. Kaushik Roy
2. Michael Pfeiffer
3. Davide Scaramuzza
4. Shih-Chii Liu
5. Timothée Masquelier
6. Yonghong Tian
7. Gopalakrishnan Srinivasan
8. Garrick Orchard

Citations in the corpus

• Deep learning in neural networks: An overview (2014, neutral): “Future energy-efficient hardware for DL in NNsmay implement aspects of such models (e.g., Fieres, Schemmel, & Meier, 2008; Glackin, McGinnity, Maguire, Wu, & Belatreche, 2005; Indiveri et al., 2011; Jin et al., 2010; Khan et al., 2008; Liu et al., 2001; Merolla et al., 2014; Neil & Liu, 2014; Roggen”
• Deep Rewiring: Training very sparse deep networks (2017, background): “…is even more critical in neuromorphic hardware, where either hard upper bounds on network connectivity are unavoidable (Schemmel et al., 2010; Merolla et al., 2014) or fast on-chip memory of local processing cores is severely limited, for example the 96 MByte lo-cal memory of cores in the…”
• Flattened Convolutional Neural Networks for Feedforward Acceleration (2014, background): “Because this is not the case always, custom architectures have been explored for power and speed efficient implementation of CNNs (Jin et al., 2014; Merolla et al., 2014).”
• Efficient Processing of Deep Neural Networks: A Tutorial and Survey (2017, background): “An example of a project that was inspired by the spiking of the brain is the IBM TrueNorth [8].”
• Neuro-inspired computing chips (2020, methodology): “91 TrueNorth 32 Ref.”
• Deep Learning With Spiking Neurons: Opportunities and Challenges (2018, methodology): “The learned probabilities are used to generate samples of deployment networks with low-bit synapses, matching the constraints of the TrueNorth target platform (Merolla et al., 2014).”
• Siamese recurrent networks learn first-order logic reasoning and exhibit zero-shot compositional generalization (2019, background): “We ask whether popular deep learning methods are capable in principle of acquiring the compositional rules that characterize it, and focus in particular on recurrent neural networks that are unambiguously ‘connectionist’: trained recurrent nets do not rely on symbolic data and control structures suc”
• Neuro-Inspired Computing With Emerging Nonvolatile Memorys (2018, methodology): “, Heidelbergs BrainScaleS [9], IBM’s TrueNorth [11], etc.”