Concept
Half-precision floating-point format
Related publications (63)
Towards General-Purpose Decentralized Computing with Permissionless Extensibility
Smart contracts have emerged as the most promising foundations for applications of the blockchain technology. Even though smart contracts are expected to serve as the backbone of the next-generation web, they have several limitations that hinder their wide ...
EPFL2024Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2
Aleksandra Radenovic, Andras Kis, Mukesh Kumar Tripathi, Zhenyu Wang, Guilherme Migliato Marega
Machine learning and signal processing on the edge are poised to influence our everyday lives with devices that will learn and infer from data generated by smart sensors and other devices for the Internet of Things. The next leap toward ubiquitous electron ...
2022ColTraIn: Co-located DNN training and inference
Mario Paulo Drumond Lages De Oliveira
Deep neural network inference accelerators are deployed at scale to accommodate online services, but face low average load because of service demand variability, leading to poor resource utilization. Unfortunately, reclaiming inference idle cycles is diffi ...
EPFL2020High Performance Computing for gravitational lens modeling: Single vs double precision on GPUs and CPUs
Jean-Paul Richard Kneib, Markus Rexroth
Strong gravitational lensing is a powerful probe of cosmology and the dark matter distribution. Efficient lensing software is already a necessity to fully use its potential and the performance demands will only increase with the upcoming generation of tele ...
ELSEVIER2020Mixed-Precision Deep Learning Based on Computational Memory
Irem Boybat Kara, Evangelos Eleftheriou, Abu Sebastian
Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have achieved unprecedented success in cognitive tasks such as image and speech recognition. Training of large DNNs, however, is computationally intensive and this has ...
FRONTIERS MEDIA SA2020ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning
Irem Boybat Kara, Evangelos Eleftheriou, Abu Sebastian
Deep neural networks (DNNs) have surpassed human-level accuracy in a variety of cognitive tasks but at the cost of significant memory/time requirements in DNN training. This limits their deployment in energy and memory limited applications that require rea ...
IEEE2020Lactate measurement by neurochemical profiling in the dorsolateral prefrontal cortex at 7T: accuracy, precision, and relaxation times
Pierre Magistretti, Lijing Xin, Kim Do, Masoumeh Dehghani Moghadam
Purpose This assesses the potential of measuring lactate in the human brain using three non-editing MRS methods at 7T and compares the accuracy and precision of the methods. Methods H-1 MRS data were measured in the right dorsolateral prefrontal cortex usi ...
2020Self-Binarizing Networks
Sabine Süsstrunk, Radhakrishna Achanta, Fayez Lahoud, Pablo Marquez Neila
We present a method to train self-binarizing neural networks, that is, networks that evolve their weights and activations during training to become binary. To obtain similar binary networks, existing methods rely on the sign activation function. This funct ...
arXiv2019Review and Benchmarking of Precision-Scalable Multiply-Accumulate Unit Architectures for Embedded Neural-Network Processing
Christian Enz, Vincent Frédéric Camus
The current trend for deep learning has come with an enormous computational need for billions of Multiply-Accumulate (MAC) operations per inference. Fortunately, reduced precision has demonstrated large benefits with low impact on accuracy, paving the way ...
2019Survey of Precision-Scalable Multiply-Accumulate Units for Neural-Network Processing
Christian Enz, Vincent Frédéric Camus
The current trend for deep learning has come with an enormous computational need for billions of Multiply-Accumulate (MAC) operations per inference. Fortunately, reduced precision has demonstrated large benefits with low impact on accuracy, paving the way ...
IEEE2019