Stochastic rounding: implementation, error analysis and applications
Stochastic rounding (SR) randomly maps a real number x to one of the two nearest values in
a finite precision number system. The probability of choosing either of these two numbers is …
a finite precision number system. The probability of choosing either of these two numbers is …
Ferroelectric-based synapses and neurons for neuromorphic computing
The shift towards a distributed computing paradigm, where multiple systems acquire and
elaborate data in real-time, leads to challenges that must be met. In particular, it is becoming …
elaborate data in real-time, leads to challenges that must be met. In particular, it is becoming …
Stochastic rounding and its probabilistic backward error analysis
Stochastic rounding rounds a real number to the next larger or smaller floating-point number
with probabilities 1 minus the relative distances to those numbers. It is gaining attention in …
with probabilities 1 minus the relative distances to those numbers. It is gaining attention in …
A simple federated learning-based scheme for security enhancement over Internet of Medical Things
Z Xu, Y Guo, C Chakraborty, Q Hua… - IEEE Journal of …, 2022 - ieeexplore.ieee.org
Nowadays, Federated Learning (FL) over Internet of Medical Things (IoMT) devices has
become a current research hotspot. As a new architecture, FL can well protect the data …
become a current research hotspot. As a new architecture, FL can well protect the data …
Q-spinn: A framework for quantizing spiking neural networks
RVW Putra, M Shafique - 2021 International Joint Conference …, 2021 - ieeexplore.ieee.org
A prominent technique for reducing the memory footprint of Spiking Neural Networks (SNNs)
without decreasing the accuracy significantly is quantization. However, the state-of-the-art …
without decreasing the accuracy significantly is quantization. However, the state-of-the-art …
Utilizing the Switching Stochasticity of HfO2/TiOx-Based ReRAM Devices and the Concept of Multiple Device Synapses for the Classification of Overlapping and …
With the arrival of the Internet of Things (IoT) and the challenges arising from Big Data,
neuromorphic chip concepts are seen as key solutions for coping with the massive amount …
neuromorphic chip concepts are seen as key solutions for coping with the massive amount …
lpspikecon: Enabling low-precision spiking neural network processing for efficient unsupervised continual learning on autonomous agents
RVW Putra, M Shafique - 2022 International Joint Conference …, 2022 - ieeexplore.ieee.org
Recent advances have shown that Spiking Neural Network (SNN)-based systems can
efficiently perform unsuper-vised continual learning due to their bio-plausible learning rule …
efficiently perform unsuper-vised continual learning due to their bio-plausible learning rule …
EnforceSNN: Enabling resilient and energy-efficient spiking neural network inference considering approximate DRAMs for embedded systems
Spiking Neural Networks (SNNs) have shown capabilities of achieving high accuracy under
unsupervised settings and low operational power/energy due to their bio-plausible …
unsupervised settings and low operational power/energy due to their bio-plausible …
Fluid simulations accelerated with 16 bits: Approaching 4x speedup on A64FX by squeezing ShallowWaters. jl into Float16
Most Earth‐system simulations run on conventional central processing units in 64‐bit double
precision floating‐point numbers Float64, although the need for high‐precision calculations …
precision floating‐point numbers Float64, although the need for high‐precision calculations …