ALBSNN: ultra-low latency adaptive local binary spiking neural network with accuracy loss estimator
Frontiers in Neuroscience, 2023 Spiking neural network (SNN) is a brain-inspired model with more spatio-temporal information processing capacity and computational energy efficiency.
Yijian Pei +5 more
doaj +1 more source
On the role of synaptic stochasticity in training low-precision neural networks [PDF]
, 2018 Stochasticity and limited precision of synaptic weights in neural network models are key aspects of both biological and hardware modeling of learning processes.
Baldassi, Carlo +6 more
core +4 more sources
Neuron-level fuzzy memoization in RNNs [PDF]
, 2019 The final publication is available at ACM via http://dx.doi.org/10.1145/3352460.3358309Recurrent Neural Networks (RNNs) are a key technology for applications such as automatic speech recognition or machine translation.
Arnau Montañés, José María +3 more
core +1 more source
Partially Binarized Deep MUSIC for Multiple Target Angle Estimation Using Wireless Sensor Array Systems [PDF]
Journal of Electromagnetic Engineering and ScienceIn this paper, a partially binarized deep learning-based MUltiple SIgnal Classification (MUSIC) algorithm for estimating the angle-of-arrivals (AoAs) of multiple targets using wireless sensor array systems is proposed.
Jongsung Kang +4 more
doaj +1 more source
MOBIUS: Model-Oblivious Binarized Neural Networks [PDF]
IEEE Access, 2019 A privacy-preserving framework in which a computational resource provider receives encrypted data from a client and returns prediction results without decrypting the data, i.e., oblivious neural network or encrypted prediction, has been studied in machine learning that provides prediction services.
Hiromasa Kitai +9 more
openaire +3 more sources
A neural network based background calibration for pipelined‐SAR ADCs at low hardware cost
Electronics Letters, 2023 This paper proposes a background calibration scheme for the pipelined‐Successive Approximation Register (SAR) Analog‐to‐Digital Converter (ADC) based on the neural network.
Yuguo Xiang +5 more
doaj +1 more source
Hardware-efficient on-line learning through pipelined truncated-error backpropagation in binary-state networks [PDF]
, 2017 Artificial neural networks (ANNs) trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks.
Cauwenberghs, Gert +3 more
core +2 more sources
Portfolio solver for verifying Binarized Neural Networks [PDF]
Annales Mathematicae et Informaticae, 2021 Although deep learning is a very successful AI technology, many concerns have been raised about to what extent the decisions making process of deep neural networks can be trusted. Verifying of properties of neural networks such as adversarial robustness and network equivalence sheds light on the trustiness of such systems.
Kovásznai, Gergely +2 more
openaire +3 more sources
OxRAM + OTS optimization for binarized neural network hardware implementation [PDF]
Semiconductor Science and Technology, 2021 Abstract Low-power memristive devices embedded on graphics or central processing units logic core are a very promising non-von-Neumann approach to improve significantly the speed and power consumption of deep learning accelerators, enhancing their deployment on embedded systems.
Minguet Lopez, Joel +13 more
openaire +3 more sources
Memristor crossbar array for binarized neural networks
AIP Advances, 2019 Memristor crossbar arrays were fabricated based on a Ti/HfO2/Ti stack that exhibited electroforming-free behavior and low device variability in a 10 x 10 array size.
Yong Kim +7 more
doaj +1 more source