Alberto Delmas Lascorz

Proceedings of the IEEE International Symposium on Circuits and Systems, 2024

Atalanta: A Bit is Worth a "Thousand" Tensor Values.

[BibT_eX]

[DOI]

Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2024

2022

APack: Off-Chip, Lossless Data Compression for Efficient Deep Learning Inference.

[BibT_eX]

[DOI]

CoRR, 2022

2021

Boveda: Building an On-Chip Deep Learning Memory Hierarchy Brick by Brick.

[BibT_eX]

[DOI]

Gennady Pekhimenko

Proceedings of the Fourth Conference on Machine Learning and Systems, 2021

2020

BitPruning: Learning Bitlengths for Aggressive and Accurate Quantization.

[BibT_eX]

[DOI]

Milos Nikolic

Ghouthi Boukli Hacene

Ciaran Bannon

CoRR, 2020

Late Breaking Results: Building an On-Chip Deep Learning Memory Hierarchy Brick by Brick.

[BibT_eX]

[DOI]

Proceedings of the 57th ACM/IEEE Design Automation Conference, 2020

2019

Accelerating Image-Sensor-Based Deep Learning Applications.

[BibT_eX]

[DOI]

Dylan Malone Stuart

Zissis Poulos

IEEE Micro, 2019

ShapeShifter: Enabling Fine-Grain Data Width Adaptation in Deep Learning.

[BibT_eX]

[DOI]

Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture, 2019

Laconic deep learning inference acceleration.

[BibT_eX]

[DOI]

Proceedings of the 46th International Symposium on Computer Architecture, 2019

Bit-Tactical: A Software/Hardware Approach to Exploiting Value and Bit Sparsity in Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems, 2019

2018

Value-Based Deep-Learning Acceleration.

[BibT_eX]

[DOI]

Jorge Albericio

Natalie D. Enright Jerger

Tayler H. Hetherington

Tor M. Aamodt

IEEE Micro, 2018

Laconic Deep Learning Computing.

[BibT_eX]

[DOI]

Milos Nikolic

CoRR, 2018

DPRed: Making Typical Activation Values Matter In Deep Learning Computing.

[BibT_eX]

[DOI]

CoRR, 2018

Bit-Tactical: Exploiting Ineffectual Computations in Convolutional Neural Networks: Which, Why, and How.

[BibT_eX]

[DOI]

CoRR, 2018

Exploiting Typical Values to Accelerate Deep Learning.

[BibT_eX]

[DOI]

Jorge Albericio

Natalie D. Enright Jerger

Zissis Poulos

Tayler H. Hetherington

Tor M. Aamodt

Computer, 2018

Identifying and Exploiting Ineffectual Computations to Enable Hardware Acceleration of Deep Learning.

[BibT_eX]

[DOI]

Tayler H. Hetherington

Natalie D. Enright Jerger

Proceedings of the 16th IEEE International New Circuits and Systems Conference, 2018

Loom: exploiting weight and activation precisions to accelerate convolutional neural networks.

[BibT_eX]

[DOI]

Kevin Siu

Proceedings of the 55th Annual Design Automation Conference, 2018

2017

Loom: Exploiting Weight and Activation Precisions to Accelerate Convolutional Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2017

Cnvlutin2: Ineffectual-Activation-and-Weight-Free Deep Neural Network Computing.

[BibT_eX]

[DOI]

CoRR, 2017

Tartan: Accelerating Fully-Connected and Convolutional Layers in Deep Learning Networks by Exploiting Numerical Precision Variability.

[BibT_eX]

[DOI]

CoRR, 2017

Dynamic Stripes: Exploiting the Dynamic Precision Requirements of Activation Values in Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2017

IDEAL: image denoising accelerator.

[BibT_eX]

[DOI]

Bojian Zheng