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1606.05487
Cited By

YodaNN: An Architecture for Ultra-Low Power Binary-Weight CNN
Acceleration

v1v2v3v4 (latest)

YodaNN: An Architecture for Ultra-Low Power Binary-Weight CNN Acceleration

17 June 2016

Lukas Cavigelli

Luca Benini

ArXiv (abs)PDF HTML

Papers citing "YodaNN: An Architecture for Ultra-Low Power Binary-Weight CNN Acceleration"

49 / 49 papers shown

RedBit: An End-to-End Flexible Framework for Evaluating the Accuracy of
Quantized CNNs

RedBit: An End-to-End Flexible Framework for Evaluating the Accuracy of Quantized CNNs

A. M. Ribeiro-dos-Santos

João Dinis Ferreira

291

4

0

15 Jan 2023

Enable Deep Learning on Mobile Devices: Methods, Systems, and
Applications

Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications

Zhijian Liu

Song Han

288

138

0

25 Apr 2022

Hardware Approximate Techniques for Deep Neural Network Accelerators: A
Survey

Hardware Approximate Techniques for Deep Neural Network Accelerators: A SurveyACM Computing Surveys (ACM CSUR), 2022

Giorgos Armeniakos

Georgios Zervakis

Dimitrios Soudris

589

136

0

16 Mar 2022

R2F: A Remote Retraining Framework for AIoT Processors with Computing
Errors

R2F: A Remote Retraining Framework for AIoT Processors with Computing Errors

Kwang-Ting Cheng

148

10

0

07 Jul 2021

Measuring what Really Matters: Optimizing Neural Networks for TinyML

Measuring what Really Matters: Optimizing Neural Networks for TinyML

256

33

0

21 Apr 2021

Hardware and Software Optimizations for Accelerating Deep Neural
Networks: Survey of Current Trends, Challenges, and the Road Ahead

Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road AheadIEEE Access (IEEE Access), 2020

Beatrice Bussolino

Alberto Marchisio

Maurizio Martina

333

179

0

21 Dec 2020

Running Neural Networks on the NIC

Running Neural Networks on the NIC

Mohammad Malekzadeh

Hamed Haddadi

153

28

0

04 Sep 2020

Training Deep Neural Networks with Constrained Learning Parameters

Training Deep Neural Networks with Constrained Learning ParametersInternational Conference on Rebooting Computing (ICRC), 2020

James A. Hendler

M. Magdon-Ismail

155

1

0

01 Sep 2020

Automated Design Space Exploration for optimised Deployment of DNN on
Arm Cortex-A CPUs

Automated Design Space Exploration for optimised Deployment of DNN on Arm Cortex-A CPUs

Miguel de Prado

Luca Benini

234

11

0

09 Jun 2020

Quantized Neural Networks: Characterization and Holistic Optimization

Quantized Neural Networks: Characterization and Holistic OptimizationIEEE Workshop on Signal Processing Systems (SiPS), 2020

233

9

0

31 May 2020

An Experimental Study of Reduced-Voltage Operation in Modern FPGAs for
Neural Network Acceleration

An Experimental Study of Reduced-Voltage Operation in Modern FPGAs for Neural Network AccelerationDependable Systems and Networks (DSN), 2020

Erhan Baturay Onural

Ismail Emir Yüksel

H. Sarbazi-Azad

328

51

0

04 May 2020

A scalable and efficient convolutional neural network accelerator using
HLS for a System on Chip design

A scalable and efficient convolutional neural network accelerator using HLS for a System on Chip design

Daniel Ejnar Larsen

165

1

0

27 Apr 2020

Improving Efficiency in Neural Network Accelerator Using Operands
Hamming Distance optimization

Improving Efficiency in Neural Network Accelerator Using Operands Hamming Distance optimizationAsia and South Pacific Design Automation Conference (ASP-DAC), 2020

149

3

0

13 Feb 2020

TentacleNet: A Pseudo-Ensemble Template for Accurate Binary
Convolutional Neural Networks

TentacleNet: A Pseudo-Ensemble Template for Accurate Binary Convolutional Neural NetworksInternational Conference on Artificial Intelligence Circuits and Systems (AICAS), 2019

223

5

0

20 Dec 2019

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network
Inference at the Edge of the Internet of Things

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of ThingsIEEE Internet of Things Journal (IEEE IoT Journal), 2019

Michele Magno

Lukas Cavigelli

Luca Benini

385

137

0

08 Nov 2019

EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference
and Training Accelerators

EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training AcceleratorsIEEE Journal on Emerging and Selected Topics in Circuits and Systems (JESTCAS), 2019

Lukas Cavigelli

Georg Rutishauser

Luca Benini

271

40

0

30 Aug 2019

Distributed Deep Convolutional Neural Networks for the
Internet-of-Things

Distributed Deep Convolutional Neural Networks for the Internet-of-ThingsIEEE transactions on computers (IEEE Trans. Comput.), 2019

Simone Disabato

250

60

0

02 Aug 2019

Processing-In-Memory Acceleration of Convolutional Neural Networks for
Energy-Efficiency, and Power-Intermittency Resilience

Processing-In-Memory Acceleration of Convolutional Neural Networks for Energy-Efficiency, and Power-Intermittency Resilience

99

18

0

16 Apr 2019

Quantized Guided Pruning for Efficient Hardware Implementations of
Convolutional Neural Networks

Quantized Guided Pruning for Efficient Hardware Implementations of Convolutional Neural Networks

Vincent Gripon

Nicolas Farrugia

138

14

0

29 Dec 2018

Distill-Net: Application-Specific Distillation of Deep Convolutional
Neural Networks for Resource-Constrained IoT Platforms

Distill-Net: Application-Specific Distillation of Deep Convolutional Neural Networks for Resource-Constrained IoT Platforms

Mohammad Motamedi

89

3

0

16 Dec 2018

Towards Fast and Energy-Efficient Binarized Neural Network Inference on
FPGA

Towards Fast and Energy-Efficient Binarized Neural Network Inference on FPGA

237

33

0

04 Oct 2018

Extended Bit-Plane Compression for Convolutional Neural Network
Accelerators

Extended Bit-Plane Compression for Convolutional Neural Network Accelerators

Lukas Cavigelli

Luca Benini

213

21

0

01 Oct 2018

CBinfer: Exploiting Frame-to-Frame Locality for Faster Convolutional
Network Inference on Video Streams

CBinfer: Exploiting Frame-to-Frame Locality for Faster Convolutional Network Inference on Video Streams

Lukas Cavigelli

Luca Benini

210

27

0

15 Aug 2018

Energy-based Tuning of Convolutional Neural Networks on Multi-GPUs

Energy-based Tuning of Convolutional Neural Networks on Multi-GPUs

Nicolás Guil Mata

M. Marín-Jiménez

Jesús Pérez Serrano

183

20

0

01 Aug 2018

PCNNA: A Photonic Convolutional Neural Network Accelerator

PCNNA: A Photonic Convolutional Neural Network Accelerator

Yousra Alkabani

224

77

0

23 Jul 2018

NullaNet: Training Deep Neural Networks for Reduced-Memory-Access
Inference

NullaNet: Training Deep Neural Networks for Reduced-Memory-Access Inference

152

22

0

23 Jul 2018

Exploration of Low Numeric Precision Deep Learning Inference Using Intel
FPGAs

Exploration of Low Numeric Precision Deep Learning Inference Using Intel FPGAs

Philip Colangelo

Eriko Nurvitadhi

138

1

0

12 Jun 2018

Accelerating CNN inference on FPGAs: A Survey

Accelerating CNN inference on FPGAs: A Survey

Jocelyn Serot

164

158

0

26 May 2018

SIPs: Succinct Interest Points from Unsupervised Inlierness Probability
Learning

SIPs: Succinct Interest Points from Unsupervised Inlierness Probability Learning

Titus Cieslewski

Konstantinos G. Derpanis

Davide Scaramuzza

290

7

0

03 May 2018

Universal and Succinct Source Coding of Deep Neural Networks

Universal and Succinct Source Coding of Deep Neural Networks

134

3

0

09 Apr 2018

MT-Spike: A Multilayer Time-based Spiking Neuromorphic Architecture with
Temporal Error Backpropagation

MT-Spike: A Multilayer Time-based Spiking Neuromorphic Architecture with Temporal Error Backpropagation

171

32

0

14 Mar 2018

PT-Spike: A Precise-Time-Dependent Single Spike Neuromorphic
Architecture with Efficient Supervised Learning

PT-Spike: A Precise-Time-Dependent Single Spike Neuromorphic Architecture with Efficient Supervised LearningAsia and South Pacific Design Automation Conference (ASP-DAC), 2018

127

11

0

14 Mar 2018

CSRNet: Dilated Convolutional Neural Networks for Understanding the
Highly Congested Scenes

CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

634

1,565

0

27 Feb 2018

An Area and Energy Efficient Design of Domain-Wall Memory-Based Deep
Convolutional Neural Networks using Stochastic Computing

An Area and Energy Efficient Design of Domain-Wall Memory-Based Deep Convolutional Neural Networks using Stochastic Computing

229

19

0

03 Feb 2018

Bit Fusion: Bit-Level Dynamically Composable Architecture for
Accelerating Deep Neural Networks

Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Networks

H. Esmaeilzadeh

297

551

0

05 Dec 2017

LightNN: Filling the Gap between Conventional Deep Neural Networks and
Binarized Networks

LightNN: Filling the Gap between Conventional Deep Neural Networks and Binarized Networks

Diana Marculescu

236

38

0

02 Dec 2017

Tactics to Directly Map CNN graphs on Embedded FPGAs

Tactics to Directly Map CNN graphs on Embedded FPGAs

Jocelyn Sérot

Jocelyn Serot

121

64

0

20 Nov 2017

ReBNet: Residual Binarized Neural Network

ReBNet: Residual Binarized Neural Network

Mohammad Samragh

212

5

0

03 Nov 2017

Balanced Quantization: An Effective and Efficient Approach to Quantized
Neural Networks

Balanced Quantization: An Effective and Efficient Approach to Quantized Neural Networks

Shuchang Zhou

215

115

0

22 Jun 2017

BMXNet: An Open-Source Binary Neural Network Implementation Based on
MXNet

BMXNet: An Open-Source Binary Neural Network Implementation Based on MXNet

Martin Fritzsche

Christian Bartz

Christoph Meinel

165

64

0

27 May 2017

CBinfer: Change-Based Inference for Convolutional Neural Networks on
Video Data

CBinfer: Change-Based Inference for Convolutional Neural Networks on Video Data

Lukas Cavigelli

Luca Benini

233

52

0

14 Apr 2017

Efficient Processing of Deep Neural Networks: A Tutorial and Survey

Efficient Processing of Deep Neural Networks: A Tutorial and Survey

456

3,522

0

27 Mar 2017

Scaling Binarized Neural Networks on Reconfigurable Logic

Scaling Binarized Neural Networks on Reconfigurable Logic

Nicholas J. Fraser

Giulio Gambardella

Philip H. W. Leong

252

60

0

12 Jan 2017

Hardware for Machine Learning: Challenges and Opportunities

Hardware for Machine Learning: Challenges and OpportunitiesIEEE Custom Integrated Circuits Conference (CICC), 2016

Amr Suleiman

Zhengdong Zhang

460

79

0

22 Dec 2016

An IoT Endpoint System-on-Chip for Secure and Energy-Efficient
Near-Sensor Analytics

An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor AnalyticsIEEE Transactions on Circuits and Systems Part 1: Regular Papers (TCAS-I), 2016

Francesco Conti

Pasquale Davide Schiavone

...

Michael Gautschi

Luca Benini

271

120

0

18 Dec 2016

FINN: A Framework for Fast, Scalable Binarized Neural Network Inference

FINN: A Framework for Fast, Scalable Binarized Neural Network Inference

Nicholas J. Fraser

Giulio Gambardella

Philip H. W. Leong

320

1,130

0

01 Dec 2016

SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using
Stochastic Computing

SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing

Caiwen Ding

205

208

0

18 Nov 2016

Computationally Efficient Target Classification in Multispectral Image
Data with Deep Neural Networks

Computationally Efficient Target Classification in Multispectral Image Data with Deep Neural Networks

Lukas Cavigelli

Dominic Bernath

Michele Magno

Luca Benini

286

21

0

09 Nov 2016

Quantized Neural Networks: Training Neural Networks with Low Precision
Weights and Activations

Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations

Matthieu Courbariaux

569

2,064

0

22 Sep 2016

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