Publications related to Decimal floating point

A 16-bit Floating-Point Near-SRAM Architecture for Low-power Sparse Matrix-Vector Multiplication

David Atienza Alonso, Giovanni Ansaloni, Grégoire Axel Eggermann, Marco Antonio Rios

State-of-the-art Artificial Intelligence (AI) algorithms, such as graph neural networks and recommendation systems, require floating-point computation of very large matrix multiplications over sparse data. Their execution in resource-constrained scenarios, ...

2023

Efficient Lung Nodule Detection via 3D Deep Learning with Shifted Convolutions

Jiancheng Yang, Bo Du, Kang Yuan

The high computational costs of deep convolutional neural networks hinder their deployment in real-world applications, including pulmonary nodule detection from CT scans where large 3D image sizes amplify the issue. This paper presents a novel 3D method to ...

IEEE2023

A Newton Frank-Wolfe method for constrained self-concordant minimization

Volkan Cevher, Quoc Tran Dinh

We develop a new Newton Frank-Wolfe algorithm to solve a class of constrained self-concordant minimization problems using linear minimization oracles (LMO). Unlike L-smooth convex functions, where the Lipschitz continuity of the objective gradient holds gl ...

SPRINGER2021

Non-Hermitian time evolution: From static to parametric instability

Romain Christophe Rémy Fleury, Aleksi Antoine Bossart

Eigenmode coalescence imparts remarkable properties to non-Hermitian time evolution, culminating in a purely non-Hermitian spectral degeneracy known as an exceptional point (EP). Here, we revisit time evolution around EPs, looking at both static and period ...

2021

ColTraIn: 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 ...

EPFL2020

Mixed-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 SA2020

ESSOP: 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 ...

IEEE2020

Two-dimensional O(n) models and logarithmic CFTs

Victor Gorbenko, Bernardo Zan

We study O(n)-symmetric two-dimensional conformal field theories (CFTs) for a continuous range of n below two. These CFTs describe the fixed point behavior of self-avoiding loops. There is a pair of known fixed points connected by an RG flow. When n is equ ...

SPRINGER2020

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Decimal floating point

Towards General-Purpose Decentralized Computing with Permissionless Extensibility

A 16-bit Floating-Point Near-SRAM Architecture for Low-power Sparse Matrix-Vector Multiplication

Efficient Lung Nodule Detection via 3D Deep Learning with Shifted Convolutions

Dynamically crossing diabolic points while encircling exceptional curves: A programmable symmetric-asymmetric multimode switch

A Newton Frank-Wolfe method for constrained self-concordant minimization

Non-Hermitian time evolution: From static to parametric instability

ColTraIn: Co-located DNN training and inference

Mixed-Precision Deep Learning Based on Computational Memory

ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning

Two-dimensional O(n) models and logarithmic CFTs

Graph Chatbot

Chat with Graph Search

Towards General-Purpose Decentralized Computing with Permissionless Extensibility

Dynamically crossing diabolic points while encircling exceptional curves: A programmable symmetric-asymmetric multimode switch

A 16-bit Floating-Point Near-SRAM Architecture for Low-power Sparse Matrix-Vector Multiplication

Efficient Lung Nodule Detection via 3D Deep Learning with Shifted Convolutions

A Newton Frank-Wolfe method for constrained self-concordant minimization

Non-Hermitian time evolution: From static to parametric instability

Mixed-Precision Deep Learning Based on Computational Memory

Two-dimensional O(n) models and logarithmic CFTs

ColTraIn: Co-located DNN training and inference

ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning