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Approximate computation is a new trend that explores and harnesses trade-offs between the precision and energy/power consumption of computing systems. In this project a feed-forward neural network was designed as well as several reservoir networks using different network topologies to compare the accuracy and resilience of the network against the computational complexity required
The goal of this work is to find out the impact of approximated computing on accuracy of deep neural...
Recently, analog compute-in-memory (CIM) architectures based on emerging analog non-volatile memory ...
Despite the recent advances in semiconductor technology and energy-aware system design, the overall ...
This paper investigates about the possibility to reduce power consumption in Neural Network using ap...
A new design approach, called approximate computing (AxC), leverages the flexibility provided by int...
Embedding Machine Learning enables integrating intelligence in recent application domains such as In...
The reduced benefits offered by technology scaling in the nanoscale era call for innovative design a...
International audienceA new design paradigm, Approximate Computing (AxC), has been established to in...
Approximate computing is an emerging design paradigm that leverages the intrinsic resilience of appl...
Abstract — Approximate computing has recently emerged as a promising approach to energy-efficient de...
Today, the concept of approximation in computing is becoming more and more a “hot topic” to investig...
Today, the concept of approximation in computing is becoming more and more a “hot topic” to investig...
Approximate computing frameworks configure applications so they can operate at a range of points in ...
Approximate computing trades off computation quality with the effort expended and as rising performa...
Approximate Computing (AxC) techniques allow trade-off accuracy for performance, energy, and area re...
The goal of this work is to find out the impact of approximated computing on accuracy of deep neural...
Recently, analog compute-in-memory (CIM) architectures based on emerging analog non-volatile memory ...
Despite the recent advances in semiconductor technology and energy-aware system design, the overall ...
This paper investigates about the possibility to reduce power consumption in Neural Network using ap...
A new design approach, called approximate computing (AxC), leverages the flexibility provided by int...
Embedding Machine Learning enables integrating intelligence in recent application domains such as In...
The reduced benefits offered by technology scaling in the nanoscale era call for innovative design a...
International audienceA new design paradigm, Approximate Computing (AxC), has been established to in...
Approximate computing is an emerging design paradigm that leverages the intrinsic resilience of appl...
Abstract — Approximate computing has recently emerged as a promising approach to energy-efficient de...
Today, the concept of approximation in computing is becoming more and more a “hot topic” to investig...
Today, the concept of approximation in computing is becoming more and more a “hot topic” to investig...
Approximate computing frameworks configure applications so they can operate at a range of points in ...
Approximate computing trades off computation quality with the effort expended and as rising performa...
Approximate Computing (AxC) techniques allow trade-off accuracy for performance, energy, and area re...
The goal of this work is to find out the impact of approximated computing on accuracy of deep neural...
Recently, analog compute-in-memory (CIM) architectures based on emerging analog non-volatile memory ...
Despite the recent advances in semiconductor technology and energy-aware system design, the overall ...
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