Towards Neuromorphic Learning Machines using Emerging Memory Devices with Brain-like Energy Efficiency

Saxena, Vishal; Wu, Xinyu; Srivastava, Ira; Zhu, Kehan

doi:10.20944/preprints201807.0362.v1

Cited by 8 publications

(7 citation statements)

References 44 publications

(63 reference statements)

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“…The computational advantages of using binary activations with respect to a custom hardware implementation [51]…”

Section: Discussionmentioning

confidence: 99%

A Deep Unsupervised Feature Learning Spiking Neural Network with Binarized Classification Layers for EMNIST Classification using SpykeFlow

Vaila¹,

Chiasson²,

Saxena³

2020

Preprint

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End user AI is trained on large server farms with data collected from the users. With ever increasing demand for IOT devices, there is a need for deep learning approaches that can be implemented (at the edge) in an energy efficient manner. In this work we approach this using spiking neural networks. The unsupervised learning technique of spike timing dependent plasticity (STDP) and binary activations are used to extract features from spiking input data. Gradient descent (backpropagation) is used only on the output layer to perform the training for classification. The accuracies obtained for the balanced EMNIST data set compare favorably with other approaches. The effect of stochastic gradient descent (SGD) approximations on learning capabilities of our network are also explored. We also introduce SPYKEFLOW, a PYTHON based software tool that we developed.

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“…The computational advantages of using binary activations with respect to a custom hardware implementation [51]…”

Section: Discussionmentioning

confidence: 99%

A Deep Unsupervised Feature Learning Spiking Neural Network with Binarized Classification Layers for EMNIST Classification using SpykeFlow

Vaila¹,

Chiasson²,

Saxena³

2020

Preprint

Self Cite

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“…Previously reported work shows the bandwidth of Carbon nanotube antenna is around 500GHz [11], [12].This bandwidth can provide a higher data rate and it can also provide excellent directional properties. This high operating frequency causes skin effect and it will be ignored as the amount of skin effect is negligible which in turn reduces the power dissipation [13].…”

Section: Fig1 Mesh Topology Of a Single Layer Of 3d Nocmentioning

confidence: 99%

Nano Machine-based Hybrid 3D Network-on-Chip (NoC) using 18nm tsmc Technology

Balamurugan*,

Shijukumar

2019

IJEAT

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Network-on-Chip paradigm is an emerging technology that provides communication between the Intellectual Property cores in System-on-Chip (SoC). The drastic development of nanotechnology supports the making of Intellectual property cores with small area, less power dissipation, improvement in latency and achieves high speed. The neural systems that emulate biological sensory capability on reconfigurable hardware are the key requirement in the field of Neuro-Engineering. Nano-neural connectivity technique is highly needed in order to implement reconfigurable Neural Networks (NNs) on-chip. This paper proposes a novel Nano Machine based Hybrid 3D Network-on-Chip for the emulation of a third-generation (3G) Neural Network model called Spiking Neural Network (SNN). This Hybrid Network-on-Chip uses a Nanomachine Transceiver with Carbon nanotube antenna which uses the novel pulse-based technique to transfer the packets between the subnet of NoC to reduce the latency. The experimental result of this work shows the improvement in latency of Nano machine-based NoC compared to the previously reported work for 18 nm TSMC technology.

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“…Deep learning has been used in plethora of applications like autonomous driving, cancer prediction, low power object recognition etc [2] [3] [4]. In particular, neural networks as a regression tool have been used in applications like, time series learning [5], stock prediction [6], pose estimation in computer vision [7], cost predictions [8] etc.…”

Section: Introductionmentioning

confidence: 99%

Regression with Deep Learning for Sensor Performance Optimization

Vaila

Lloyd

Tetz

2021

2021 IEEE Workshop on Microelectronics and Electron Devices (WMED)

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Neural networks with at least two hidden layers are called deep networks [1]. Recent developments in AI and computer programming in general has led to development of tools such as Tensorflow, Keras, NumPy etc. making it easier to model and draw conclusions from data. In this work we re-approach non-linear regression with deep learning enabled by Keras and Tensorflow. In particular, we use deep learning to parametrize a non-linear multivariate relationship between inputs and outputs of an industrial sensor with an intent to optimize the sensor performance based on selected key metrics.

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Towards Neuromorphic Learning Machines using Emerging Memory Devices with Brain-like Energy Efficiency

Cited by 8 publications

References 44 publications

A Deep Unsupervised Feature Learning Spiking Neural Network with Binarized Classification Layers for EMNIST Classification using SpykeFlow

A Deep Unsupervised Feature Learning Spiking Neural Network with Binarized Classification Layers for EMNIST Classification using SpykeFlow

Nano Machine-based Hybrid 3D Network-on-Chip (NoC) using 18nm tsmc Technology

Regression with Deep Learning for Sensor Performance Optimization

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