Add to ORKGPattern recognition as used in triggers for large particle physics experiments should be at the same time fast and adaptive. It has to be fast to manage the high data rates, and adaptive to optimize the performance of a trigger t
The Large Hadron Collider at CERN is the world’s most powerful particle accelerator. Data from its p...
Complex machine learning (ML) based algorithms are finding their ways to low-level hardware devices...
A strategy has been developed to computationally accelerate the response time of a generic electroni...
A novel neural chip SAND (Simple Applicable Neural Device) is described. It is highly usable for har...
After their inception in the 1940s and several decades of moderate success, artificial neural networ...
At particle colliders, more data are produced than what the experiments can store for further analys...
Targeted at high-energy physics research applications, our special-purpose analog neural processor c...
International audienceSelecting interesting proton–proton collisions from the millions taking place ...
Investigates the possibility of performing pattern recognition tasks by special purpose processors, ...
We present a short overview of neuromorphic hardware and some of the physics projects making use of ...
The application of Artificial Neural Networks in Particle Physics is reviewed. Most common is the us...
Particle physics experiments create large data streams at high rates ranging from kHz to MHz. In a s...
The application of artificial neural networks in particle physics is reviewed. The use of feed-forwa...
Artificial neural networks are becoming a standard tool for data analysis, but their potential remai...
The popularity of Deep Learning (DL) has grown exponentially in all scientific fields, included part...
The Large Hadron Collider at CERN is the world’s most powerful particle accelerator. Data from its p...
Complex machine learning (ML) based algorithms are finding their ways to low-level hardware devices...
A strategy has been developed to computationally accelerate the response time of a generic electroni...
A novel neural chip SAND (Simple Applicable Neural Device) is described. It is highly usable for har...
After their inception in the 1940s and several decades of moderate success, artificial neural networ...
At particle colliders, more data are produced than what the experiments can store for further analys...
Targeted at high-energy physics research applications, our special-purpose analog neural processor c...
International audienceSelecting interesting proton–proton collisions from the millions taking place ...
Investigates the possibility of performing pattern recognition tasks by special purpose processors, ...
We present a short overview of neuromorphic hardware and some of the physics projects making use of ...
The application of Artificial Neural Networks in Particle Physics is reviewed. Most common is the us...
Particle physics experiments create large data streams at high rates ranging from kHz to MHz. In a s...
The application of artificial neural networks in particle physics is reviewed. The use of feed-forwa...
Artificial neural networks are becoming a standard tool for data analysis, but their potential remai...
The popularity of Deep Learning (DL) has grown exponentially in all scientific fields, included part...
The Large Hadron Collider at CERN is the world’s most powerful particle accelerator. Data from its p...
Complex machine learning (ML) based algorithms are finding their ways to low-level hardware devices...
A strategy has been developed to computationally accelerate the response time of a generic electroni...