Hessian Aware Quantization V3 Dyadic Neural Network Quantization

June 11, 2026
Reference Brief: Authors: Shixing Yu (Peking University)*; Zhewei Yao (University of California, Berkeley); Amir Gholami (UC Berkeley); Zhen ... Presented by Jordan Dotzel at TECHCON2020, online Authors: Ritchie Zhao, Jordan Dotzel, Christopher De Sa, Zhiru Zhang ...

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Presented by Jordan Dotzel at TECHCON2020, online Authors: Ritchie Zhao, Jordan Dotzel, Christopher De Sa, Zhiru Zhang ... This paper presents a clever idea that different layers should apply different precision.

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Authors: Shixing Yu (Peking University)*; Zhewei Yao (University of California, Berkeley); Amir Gholami (UC Berkeley); Zhen ...

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  • Authors: Shixing Yu (Peking University)*; Zhewei Yao (University of California, Berkeley); Amir Gholami (UC Berkeley); Zhen ...
  • Presented by Jordan Dotzel at TECHCON2020, online Authors: Ritchie Zhao, Jordan Dotzel, Christopher De Sa, Zhiru Zhang ...
  • This paper presents a clever idea that different layers should apply different precision.

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Hessian AWare Quantization V3: Dyadic Neural Network Quantization
HAWQ-V3: Dyadic Neural Network Quantization
Quantization explained with PyTorch - Post-Training Quantization, Quantization-Aware Training
[TECHCON'20] Overwrite Quantization: Opportunistic Outlier Handling for Neural Network Accelerators
Hessian-Aware Pruning and Optimal Neural Implant
DAC 2020 30.3 - Learning to Quantize Deep Neural Networks: A Competitive-Collaborative Approach
Hessian Aware Quantization, Zero-shot Quantization 01
Energy Profiling of Neural Network Quantization Schemes for GPUs
What is LLM quantization?
How LLMs survive in low precision | Quantization Fundamentals
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Review Key Notes
Hessian AWare Quantization V3: Dyadic Neural Network Quantization

Hessian AWare Quantization V3: Dyadic Neural Network Quantization

Read more details and related context about Hessian AWare Quantization V3: Dyadic Neural Network Quantization.

HAWQ-V3: Dyadic Neural Network Quantization

HAWQ-V3: Dyadic Neural Network Quantization

This talk is a part of Deep Learning Compiler Study. To watch the others, please refer to here: ...

Quantization explained with PyTorch - Post-Training Quantization, Quantization-Aware Training

Quantization explained with PyTorch - Post-Training Quantization, Quantization-Aware Training

Read more details and related context about Quantization explained with PyTorch - Post-Training Quantization, Quantization-Aware Training.

[TECHCON'20] Overwrite Quantization: Opportunistic Outlier Handling for Neural Network Accelerators

[TECHCON'20] Overwrite Quantization: Opportunistic Outlier Handling for Neural Network Accelerators

Presented by Jordan Dotzel at TECHCON2020, online Authors: Ritchie Zhao, Jordan Dotzel, Christopher De Sa, Zhiru Zhang ...

Hessian-Aware Pruning and Optimal Neural Implant

Hessian-Aware Pruning and Optimal Neural Implant

Authors: Shixing Yu (Peking University)*; Zhewei Yao (University of California, Berkeley); Amir Gholami (UC Berkeley); Zhen ...

DAC 2020 30.3 - Learning to Quantize Deep Neural Networks: A Competitive-Collaborative Approach

DAC 2020 30.3 - Learning to Quantize Deep Neural Networks: A Competitive-Collaborative Approach

This paper presents a clever idea that different layers should apply different precision. They've shown promising results by using ...

Hessian Aware Quantization, Zero-shot Quantization 01

Hessian Aware Quantization, Zero-shot Quantization 01

Read more details and related context about Hessian Aware Quantization, Zero-shot Quantization 01.

Energy Profiling of Neural Network Quantization Schemes for GPUs

Energy Profiling of Neural Network Quantization Schemes for GPUs

Read more details and related context about Energy Profiling of Neural Network Quantization Schemes for GPUs.

What is LLM quantization?

What is LLM quantization?

Read more details and related context about What is LLM quantization?.

How LLMs survive in low precision | Quantization Fundamentals

How LLMs survive in low precision | Quantization Fundamentals

Read more details and related context about How LLMs survive in low precision | Quantization Fundamentals.