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About Topic In Short: |
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Who: Institute Name - Technion–Israel Institute
of Technology and the Peng Cheng Laboratory; Authors - Shahar Kvatinsky and
colleagues. |
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What: Innovation or Research: A neuromorphic
computing system supporting deep belief neural networks (DBNs) based on
silicon memristive synapses. |
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How: The system utilizes silicon-based memristors
to emulate human brain synapses, overcoming the limitations of memristor
availability by using a commercially available Flash technology engineered to
behave like a memristor. The system is specifically tested with a
binary-based DBN that eliminates the need for data conversions. |
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Introduction:
In the realm of
Artificial Intelligence (AI), considerable strides have been made; however, the
challenge remains with energy-intensive training and computation on
conventional hardware. To tackle this obstacle, researchers from
Technion–Israel Institute of Technology and the Peng Cheng Laboratory have
crafted a neuromorphic computing system that empowers Deep Belief Neural
Networks (DBNs), a profound class of deep learning models. This groundbreaking
system harnesses silicon-based memristors, remarkable energy-efficient devices proficient
in information storage and processing. Within this article, we embark on the
journey of unveiling the intricate process that births this innovative deep
belief neural network, driven by silicon memristive synapses.
Comprehending
Memristors and Neuromorphic Computing:
Behold
memristors, these electrical components wield dominion over electrical current
in circuits while retaining the charge that courses through their core. In
their likeness to human brain synapses, they proffer a captivating substitute
for running AI models. Embracing neuromorphic computing with memristors has
confronted challenges, chiefly the scarcity of memristive technology and the
exorbitant cost of converting analog computations to digital data and back.
Conquering
Obstacles and Constructing the Neuromorphic System:
The valiant
efforts of Shahar Kvatinsky and his adept team have brought forth a
neuromorphic computing system fashioned from commercially available Flash
technology sourced from Tower Semiconductor. Ingeniously tweaked to mimic
memristors, this technology overcomes the scarcity conundrum. Furthermore, a
carefully selected, freshly devised DBN epitomizes the system, inherently
processing binary input and output data, eradicating the necessity for
conversions.
Comprehending
Deep Belief Neural Networks (DBNs):
Glorious DBNs, a
splendid breed of generative and graphical deep learning models, bear gifts of
uniqueness unlike conventional deep neural networks. Behold their training,
wherein the accumulation of desired model updates occurs, only to be unleashed
upon reaching a specific threshold. The artistry of DBNs, adorned in simplicity
and binary essence, renders them irresistible for hardware implementation.
Crafting
Artificial Synapses with Memristive Silicon:
Employing
wondrous commercial complementary-metal-oxide-semiconductor (CMOS) processes,
the researchers forge artificial synapses of the silicon-based memristive kin.
These gifted synapses boast a cornucopia of splendid traits - analog
tunability, unyielding endurance, longevity of retention, foretold cycling
degradation, and moderate variance across devices.
Dazzling
Demonstration of the Neuromorphic System:
Wondrous feats
ensue as the team demonstrates the system's might, training a restricted
Boltzmann machine - a DBN variant - to partake in pattern recognition. Behold,
a dazzling spectacle, for the model attains resplendent accuracy, surpassing
97% recognition in the realm of handwritten digits, all thanks to the Y-Flash
endowed memristors.
A Glimpse into
Energy-Efficient AI Systems:
The heralding of
this novel architecture sets forth a path ablaze with promise - the path
leading to heightened energy efficiency among AI systems, most notably in the
realms of restricted Boltzmann machines and assorted DBNs. The scalable wonder
of this architecture bequeaths the world with opportunities aplenty, beckoning
the exploration of additional memristive realms and a cornucopia of neural
network architectures.
Thus Speak
Authors/Experts:
Shahar Kvatinsky
and his venerable comrades herald the significance of their neuromorphic marvel
built upon silicon memristive synapses. Their clarion call resounds through the
halls of science, championing the conquest of limitations surrounding
memristive technology and the golden gateway to vanquish the costly converters
that mar digital and analog domains. A splendid symphony unfolds as a DBN
arises, wreathed in accuracy, bearing testament to the resplendent practicality
and operational prowess of this visionary system.
Conclusion:
Behold, the
unveiling of a deep belief neural network birthed from the heart of silicon
memristive synapses heralds a triumph for neuromorphic computing. As we embrace
memristors' bewitching prowess, a more energy-efficient era of AI model training
and execution emerges. The horizon of possibility stretches far and wide, for
the architecture's scope transcends to bolder heights, promising untold wonders
in the realm of AI and the boundless possibilities of neuromorphic systems.
Image
Gallery
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 Memristors measured in a probe station.
Credit: Technion Spokespers |
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All Images Credit: from References/Resources
sites [Internet] |
Hashtags/Keywords/Labels:
#DeepBeliefNeuralNetwork
#SiliconMemristiveSynapses #NeuromorphicComputing #AI #MachineLearning
#EnergyEfficiency #Technion #PengChengLaboratory #DBN
#RestrictedBoltzmannMachine
References/Resources:
1.
https://techxplore.com/news/2023-01-deep-belief-neural-network-based.html
2.
https://news8plus.com/a-deep-belief-neural-network-based-on-silicon-memristive-synapses/
4.
Wei Wang et al,
"A memristive deep belief neural network based on silicon synapses,"
Nature Electronics (2022). DOI: 10.1038/s41928-022-00878-9
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