TrueNorth: Accelerating From Zero to 64 Million Neurons in 10 Years

DeBole, Michael; Taba, Brian; Amir, Arnon; Akopyan, Filipp; Andreopoulos, Alexander; Risk, W. P.; Kusnitz, Jeff; Otero, Carlos Ortega; Nayak, T. K.; Appuswamy, Raja; Carlson, Peter J.; Cassidy, Andrew; Datta, Pallab; Esser, Steven K.; Garreau, Guillaume J.; Holland, Kevin L.; Lee, Scott; Mastro, Michael; McKinstry, Jeff; Nolfo, Carmelo di; Paulovicks, Brent; Sawada, Jun; Schleupen, Kai; Shaw, B. G.; Klamo, Jennifer L.; Flickner, Myron; Arthur, John V.; Modha, Dharmendra S.

doi:10.1109/mc.2019.2903009

Cited by 161 publications

(92 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The implementation stresses flexibility in the exploration of ideas and has not been refined for efficiency of execution or speed. There is a very high degree of parallelism in the model that very likely could be effectively executed in modern architecture, such as multi-core CPUs, GPUs, and emerging asynchronous and neuromorphic computing systems [ 30 ] (DeBole et al, 2019). The code is available at .…”

Section: Methodsmentioning

confidence: 99%

Thermodynamic Neural Network

Hylton

2020

Entropy

View full text Add to dashboard Cite

This work describes a thermodynamically motivated neural network model that self-organizes to transport charge associated with internal and external potentials while in contact with a thermal bath. Isolated networks show multiscale dynamics and evidence of phase transitions, and externally driven networks evolve to efficiently connect external positive and negative potentials. The model implements techniques for rapid, global, reversible, conservative equilibration of node states followed by slow, local, irreversible, dissipative adaptation of the edge states as a means to create multiscale order. All interactions in the network are local and the network structures can be generic and recurrent. The model integrates concepts of fluctuation, dissipation, adaptation and equilibration and offers an illustration of the thermodynamic evolution of organization in open systems. The key conclusion of the work is that the dissipation of conserved physical quantities can drive the self-organization of open thermodynamic systems.

show abstract

Section: Methodsmentioning

confidence: 99%

Thermodynamic Neural Network

Hylton

2020

Entropy

View full text Add to dashboard Cite

show abstract

“…TrueNorth [55] is another neuromorphic platform capable of evaluating SNNs at faster than real time and at very low power. They demonstrate running state of the art neural networks on the hardware platform scaling up to 64 million neurons and 16 billion synapses while the system consumes only 70 W of power out of which only 15 W is consumed by the neuromorphic hardware components.…”

Section: Snn Simulation Tools and Hardware Acceleratorsmentioning

confidence: 99%

Brain-Inspired Spiking Neural Networks

Ahmed¹

2021

Biomimetics

View full text Add to dashboard Cite

Brain is a very efficient computing system. It performs very complex tasks while occupying about 2 liters of volume and consuming very little energy. The computation tasks are performed by special cells in the brain called neurons. They compute using electrical pulses and exchange information between them through chemicals called neurotransmitters. With this as inspiration, there are several compute models which exist today trying to exploit the inherent efficiencies demonstrated by nature. The compute models representing spiking neural networks (SNNs) are biologically plausible, hence are used to study and understand the workings of brain and nervous system. More importantly, they are used to solve a wide variety of problems in the field of artificial intelligence (AI). They are uniquely suited to model temporal and spatio-temporal data paradigms. This chapter explores the fundamental concepts of SNNs, few of the popular neuron models, how the information is represented, learning methodologies, and state of the art platforms for implementing and evaluating SNNs along with a discussion on their applications and broader role in the field of AI and data networks.

show abstract

“…Neuromorphic computing was coined by Mead, when he envisioned that while exploiting the similarities between semiconductor physics and biological neural systems, one may develop brain‐inspired computing platforms. Ever since, neuromorphic research has evolved and researchers are implementing various technologies, from conventional semiconductors, as proposed by Mead, to memristive systems, to hybrid CMOS–memristive designs to develop neuro‐mimicking platforms for replicating experimental results observed in biology or for neuro‐inspired platforms used in computing systems …”

Section: Introductionmentioning

confidence: 99%

Complementary Metal‐Oxide Semiconductor and Memristive Hardware for Neuromorphic Computing

Azghadi

Chen

Eshraghian

et al. 2020

Advanced Intelligent Systems

View full text Add to dashboard Cite

The ever‐increasing processing power demands of digital computers cannot continue to be fulfilled indefinitely unless there is a paradigm shift in computing. Neuromorphic computing, which takes inspiration from the highly parallel, low‐power, high‐speed, and noise‐tolerant computing capabilities of the brain, may provide such a shift. Many researchers from across academia and industry have been studying materials, devices, circuits, and systems, to implement some of the functions of networks of neurons and synapses to develop neuromorphic computing platforms. These platforms are being designed using various hardware technologies, including the well‐established complementary metal‐oxide semiconductor (CMOS), and emerging memristive technologies such as SiOx‐based memristors. Herein, recent progress in CMOS, SiOx‐based memristive, and mixed CMOS‐memristive hardware for neuromorphic systems is highlighted. New and published results from various devices are provided that are developed to replicate selected functions of neurons, synapses, and simple spiking networks. It is shown that the CMOS and memristive devices are assembled in different neuromorphic learning platforms to perform simple cognitive tasks such as classification of spike rate‐based patterns or handwritten digits. Herein, it is envisioned that what is demonstrated is useful to the unconventional computing research community by providing insights into advances in neuromorphic hardware technologies.

show abstract

TrueNorth: Accelerating From Zero to 64 Million Neurons in 10 Years

Cited by 161 publications

References 18 publications

Thermodynamic Neural Network

Thermodynamic Neural Network

Brain-Inspired Spiking Neural Networks

Complementary Metal‐Oxide Semiconductor and Memristive Hardware for Neuromorphic Computing

Contact Info

Product

Resources

About