Matthew Mattina

research

∙ 01/15/2022

UDC: Unified DNAS for Compressible TinyML Models

Emerging Internet-of-things (IoT) applications are driving deployment of...

0 Igor Fedorov, et al. ∙

research

∙ 08/13/2021

Towards Efficient Point Cloud Graph Neural Networks Through Architectural Simplification

In recent years graph neural network (GNN)-based approaches have become ...

15 Shyam A. Tailor, et al. ∙

research

∙ 07/16/2021

S2TA: Exploiting Structured Sparsity for Energy-Efficient Mobile CNN Acceleration

Exploiting sparsity is a key technique in accelerating quantized convolu...

0 Zhi-Gang Liu, et al. ∙

research

∙ 02/22/2021

On the Effects of Quantisation on Model Uncertainty in Bayesian Neural Networks

Bayesian neural networks (BNNs) are making significant progress in many ...

11 Martin Ferianc, et al. ∙

research

∙ 02/14/2021

Doping: A technique for efficient compression of LSTM models using sparse structured additive matrices

Structured matrices, such as those derived from Kronecker products (KP),...

0 Urmish Thakker, et al. ∙

research

∙ 01/28/2021

Information contraction in noisy binary neural networks and its implications

Neural networks have gained importance as the machine learning models th...

0 Chuteng Zhou, et al. ∙

research

∙ 10/21/2020

MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers

Executing machine learning workloads locally on resource constrained mic...

0 Colby Banbury, et al. ∙

research

∙ 10/06/2020

Rank and run-time aware compression of NLP Applications

Sequence model based NLP applications can be large. Yet, many applicatio...

0 Urmish Thakker, et al. ∙

research

∙ 09/04/2020

Sparse Systolic Tensor Array for Efficient CNN Hardware Acceleration

Convolutional neural network (CNN) inference on mobile devices demands e...

0 Zhi-Gang Liu, et al. ∙

research

∙ 08/03/2020

High Throughput Matrix-Matrix Multiplication between Asymmetric Bit-Width Operands

Matrix multiplications between asymmetric bit-width operands, especially...

0 Dibakar Gope, et al. ∙

research

∙ 07/23/2020

Efficient Residue Number System Based Winograd Convolution

Prior research has shown that Winograd algorithm can reduce the computat...

0 Zhi-Gang Liu, et al. ∙

research

∙ 05/20/2020

TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids

Modern speech enhancement algorithms achieve remarkable noise suppressio...

0 Igor Fedorov, et al. ∙

research

∙ 05/16/2020

Systolic Tensor Array: An Efficient Structured-Sparse GEMM Accelerator for Mobile CNN Inference

Convolutional neural network (CNN) inference on mobile devices demands e...

0 Zhi-Gang Liu, et al. ∙

research

∙ 02/25/2020

Searching for Winograd-aware Quantized Networks

Lightweight architectural designs of Convolutional Neural Networks (CNNs...

0 Javier Fernandez-Marques, et al. ∙

research

∙ 01/24/2020

Compressing Language Models using Doped Kronecker Products

Kronecker Products (KP) have been used to compress IoT RNN Applications ...

0 Urmish Thakker, et al. ∙

research

∙ 01/14/2020

Noisy Machines: Understanding Noisy Neural Networks and Enhancing Robustness to Analog Hardware Errors Using Distillation

The success of deep learning has brought forth a wave of interest in com...

7 Chuteng Zhou, et al. ∙

research

∙ 11/18/2019

ISP4ML: Understanding the Role of Image Signal Processing in Efficient Deep Learning Vision Systems

Convolutional neural networks (CNNs) are now predominant components in a...

41 Patrick Hansen, et al. ∙

research

∙ 11/04/2019

Ternary MobileNets via Per-Layer Hybrid Filter Banks

MobileNets family of computer vision neural networks have fueled tremend...

0 Dibakar Gope, et al. ∙

research

∙ 10/04/2019

Pushing the limits of RNN Compression

Recurrent Neural Networks (RNN) can be difficult to deploy on resource c...

0 Urmish Thakker, et al. ∙

research

∙ 06/12/2019

Run-Time Efficient RNN Compression for Inference on Edge Devices

Recurrent neural networks can be large and compute-intensive, yet many a...

0 Urmish Thakker, et al. ∙

research

∙ 06/07/2019

Compressing RNNs for IoT devices by 15-38x using Kronecker Products

Recurrent Neural Networks (RNN) can be large and compute-intensive, maki...

0 Urmish Thakker, et al. ∙

research

∙ 05/28/2019

SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers

The vast majority of processors in the world are actually microcontrolle...

5 Igor Fedorov, et al. ∙

research

∙ 04/05/2019

Measuring scheduling efficiency of RNNs for NLP applications

Recurrent neural networks (RNNs) have shown state of the art results for...

0 Urmish Thakker, et al. ∙

research

∙ 03/04/2019

Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications

Machine learning-based applications are increasingly prevalent in IoT de...

0 Dibakar Gope, et al. ∙

research

∙ 03/04/2019

Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUs

The Winograd or Cook-Toom class of algorithms help to reduce the overall...

0 Partha Maji, et al. ∙

research

∙ 03/04/2019

Learning low-precision neural networks without Straight-Through Estimator(STE)

The Straight-Through Estimator (STE) is widely used for back-propagating...

0 Zhi-Gang Liu, et al. ∙

research

∙ 02/27/2019

FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer Learning

The computational demands of computer vision tasks based on state-of-the...

0 Paul N. Whatmough, et al. ∙

research

∙ 12/05/2018

Efficient and Robust Machine Learning for Real-World Systems

While machine learning is traditionally a resource intensive task, embed...

17 Franz Pernkopf, et al. ∙

research

∙ 12/04/2018

Energy Efficient Hardware for On-Device CNN Inference via Transfer Learning

On-device CNN inference for real-time computer vision applications can r...

0 Paul Whatmough, et al. ∙

research

∙ 10/16/2018

SCALE-Sim: Systolic CNN Accelerator Simulator

Systolic Arrays are one of the most popular compute substrates within De...

0 Ananda Samajdar, et al. ∙

research

∙ 10/16/2018

SCALE-Sim: Systolic CNN Accelerator

Systolic Arrays are one of the most popular compute substrates within De...

0 Ananda Samajdar, et al. ∙

research

∙ 03/29/2018

Euphrates: Algorithm-SoC Co-Design for Low-Power Mobile Continuous Vision

Continuous computer vision (CV) tasks increasingly rely on convolutional...

0 Yuhao Zhu, et al. ∙

research

∙ 01/19/2018

Mobile Machine Learning Hardware at ARM: A Systems-on-Chip (SoC) Perspective

Machine learning is playing an increasingly significant role in emerging...

0 Yuhao Zhu, et al. ∙

Matthew Mattina

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