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

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

Sub-Matrix Factorization for Real-Time Vote Prediction

Patrick Thiran, Matthias Grossglauser, Victor Kristof

We address the problem of predicting aggregate vote outcomes (e.g., national) from partial outcomes (e.g., regional) that are revealed sequentially. We combine matrix factorization techniques and generalized linear models (GLMs) to obtain a flexible, effic ...

2020

Multi-ReRAM synapses for artificial neural network training

Elmira Shahrabi, Igor Krawczuk, Yusuf Leblebici, Irem Boybat Kara, Carlo Ricciardi, Cecilia Giovinazzo, Evangelos Eleftheriou, Abu Sebastian

Metal-oxide-based resistive memory devices (ReRAM) are being actively researched as synaptic elements of neuromorphic co-processors for training deep neural networks (DNNs). However, device-level non-idealities are posing significant challenges. In this wo ...

IEEE2019

Non-filamentary non-volatile memory elements as synapses in neuromorphic systems

Yusuf Leblebici, Alessandro Fumarola, Severin Sidler

Crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing highly energy-efficient neuromorphic computing systems. For Deep Neural Networks (DNN), where information can be encoded as analog voltage and current levels, ...

IEEE2019

Self-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 ...

arXiv2019

A Householder-Based Algorithm For Hessenberg-Triangular Reduction

Daniel Kressner, Zvonimir Bujanovic

The QZ algorithm for computing eigenvalues and eigenvectors of a matrix pencil A - lambda B requires that the matrices first be reduced to Hessenberg-triangular (HT) form. The current method of choice for HT reduction relies entirely on Givens rotations re ...

SIAM PUBLICATIONS2018

Training DNNs with Hybrid Block Floating Point

Babak Falsafi, Martin Jaggi, Tao Lin, Mario Paulo Drumond Lages De Oliveira

The wide adoption of DNNs has given birth to unrelenting computing requirements, forcing datacenter operators to adopt domain-specific accelerators to train them. These accelerators typically employ densely packed full-precision floating-point arithmetic t ...

2018

Training DNNs with Hybrid Block Floating Point

Babak Falsafi, Martin Jaggi, Tao Lin, Mario Paulo Drumond Lages De Oliveira

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

MATHICSE Technical Report : A fast spectral divide-and-conquer method for banded matrices

Daniel Kressner, Ana Susnjara

Based on the spectral divide-and-conquer algorithm by Nakatsukasa and Higham [SIAM J. Sci. Comput., 35(3):A1325{A1349, 2013], we propose a new algorithm for computing all the eigenvalues and eigenvectors of a symmetric banded matrix. For this purpose, we c ...

MATHICSE2018

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

Mixed-Precision Deep Learning Based on Computational Memory

Sub-Matrix Factorization for Real-Time Vote Prediction

Design of approximate and precision-scalable circuits for embedded multimedia and neural-network processing

Multi-ReRAM synapses for artificial neural network training

Non-filamentary non-volatile memory elements as synapses in neuromorphic systems

Self-Binarizing Networks

A Householder-Based Algorithm For Hessenberg-Triangular Reduction

Training DNNs with Hybrid Block Floating Point

Training DNNs with Hybrid Block Floating Point

MATHICSE Technical Report : A fast spectral divide-and-conquer method for banded matrices

Graph Chatbot

Chat with Graph Search

Design of approximate and precision-scalable circuits for embedded multimedia and neural-network processing

Sub-Matrix Factorization for Real-Time Vote Prediction

Mixed-Precision Deep Learning Based on Computational Memory

Multi-ReRAM synapses for artificial neural network training

Training DNNs with Hybrid Block Floating Point

MATHICSE Technical Report : A fast spectral divide-and-conquer method for banded matrices

Self-Binarizing Networks

Non-filamentary non-volatile memory elements as synapses in neuromorphic systems

A Householder-Based Algorithm For Hessenberg-Triangular Reduction

Training DNNs with Hybrid Block Floating Point