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01/18/2023
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Despite the advancement of machine learning techniques in recent years, ...
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04/06/2021
TENT: Efficient Quantization of Neural Networks on the tiny Edge with Tapered FixEd PoiNT
In this research, we propose a new low-precision framework, TENT, to lev...
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06/22/2020
End-to-End Memristive HTM System for Pattern Recognition and Sequence Prediction
Neuromorphic systems that learn and predict from streaming inputs hold s...
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02/26/2020
Metaplasticity in Multistate Memristor Synaptic Networks
Recent studies have shown that metaplastic synapses can retain informati...
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08/06/2019
Cheetah: Mixed Low-Precision Hardware Software Co-Design Framework for DNNs on the Edge
Low-precision DNNs have been extensively explored in order to reduce the...
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07/30/2019
Deep Learning Training on the Edge with Low-Precision Posits
Recently, the posit numerical format has shown promise for DNN data repr...
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07/01/2019
Analysis of Wide and Deep Echo State Networks for Multiscale Spatiotemporal Time Series Forecasting
Echo state networks are computationally lightweight reservoir models ins...
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03/25/2019
Performance-Efficiency Trade-off of Low-Precision Numerical Formats in Deep Neural Networks
Deep neural networks (DNNs) have been demonstrated as effective prognost...
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12/27/2018
Neuromemrisitive Architecture of HTM with On-Device Learning and Neurogenesis
Hierarchical temporal memory (HTM) is a biomimetic sequence memory algor...
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12/05/2018
Deep Positron: A Deep Neural Network Using the Posit Number System
The recent surge of interest in Deep Neural Networks (DNNs) has led to i...
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08/17/2018
Neuromorphic Architecture for the Hierarchical Temporal Memory
A biomimetic machine intelligence algorithm, that holds promise in creat...
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08/01/2018
Mod-DeepESN: Modular Deep Echo State Network
Neuro-inspired recurrent neural network algorithms, such as echo state n...
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05/22/2018
Deep Learning Inference on Embedded Devices: Fixed-Point vs Posit
Performing the inference step of deep learning in resource constrained e...
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02/20/2018
On the Statistical Challenges of Echo State Networks and Some Potential Remedies
Echo state networks are powerful recurrent neural networks. However, the...
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11/03/2017
Convolutional Drift Networks for Video Classification
Analyzing spatio-temporal data like video is a challenging task that req...
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11/09/2016
Non-volatile Hierarchical Temporal Memory: Hardware for Spatial Pooling
Hierarchical Temporal Memory (HTM) is a biomimetic machine learning algo...
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01/27/2016
Unsupervised Learning in Neuromemristive Systems
Neuromemristive systems (NMSs) currently represent the most promising pl...
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01/22/2016