Stochastic

Stochastic Research

Nanoscaling Floating-Point (NxFP): NanoMantissa, Adaptive Microexponents, and Code Recycling for Direct-Cast Compression of Large Language Models

As cutting-edge large language models (LLMs) continue to transform various industries, their fast-growing model size and sequence length have led to memory traffic and capacity challenges…

Dec 15th, 2024

Nanoscaling Floating-Point (NxFP): NanoMantissa, Adaptive Microexponents, and Code Recycling for Direct-Cast Compression of Large Language Models

As cutting-edge large language models (LLMs) continue to transform various industries, their fast-growing model size and sequence length have led to memory traffic and capacity challenges…

Dec 15th, 2024

Nanoscaling Floating-Point (NxFP): NanoMantissa, Adaptive Microexponents, and Code Recycling for Direct-Cast Compression of Large Language Models

As cutting-edge large language models (LLMs) continue to transform various industries, their fast-growing model size and sequence length have led to memory traffic and capacity challenges…

Dec 15th, 2024

Nanoscaling Floating-Point (NxFP): NanoMantissa, Adaptive Microexponents, and Code Recycling for Direct-Cast Compression of Large Language Models

As cutting-edge large language models (LLMs) continue to transform various industries, their fast-growing model size and sequence length have led to memory traffic and capacity challenges…

Dec 15th, 2024

JointNF: Enhancing DNN Performance through Adaptive N: M Pruning across both Weight and Activation

Balancing accuracy and hardware efficiency remains a challenge with traditional pruning methods. N:M sparsity is a recent approach offering a compromise, allowing up to N non-zero weights…

Aug 5th, 2024

JointNF: Enhancing DNN Performance through Adaptive N: M Pruning across both Weight and Activation

Balancing accuracy and hardware efficiency remains a challenge with traditional pruning methods. N:M sparsity is a recent approach offering a compromise, allowing up to N non-zero weights…

Aug 5th, 2024

JointNF: Enhancing DNN Performance through Adaptive N: M Pruning across both Weight and Activation

Balancing accuracy and hardware efficiency remains a challenge with traditional pruning methods. N:M sparsity is a recent approach offering a compromise, allowing up to N non-zero weights…

Aug 5th, 2024

JointNF: Enhancing DNN Performance through Adaptive N: M Pruning across both Weight and Activation

Balancing accuracy and hardware efficiency remains a challenge with traditional pruning methods. N:M sparsity is a recent approach offering a compromise, allowing up to N non-zero weights…

Aug 5th, 2024

Mad-max beyond single-node: Enabling large machine learning model acceleration on distributed systems

Training and deploying large-scale machine learning models is time-consuming, requires significant distributed computing infrastructures, and incurs high operational costs…

June 29th, 2024

Mad-max beyond single-node: Enabling large machine learning model acceleration on distributed systems

Training and deploying large-scale machine learning models is time-consuming, requires significant distributed computing infrastructures, and incurs high operational costs…

June 29th, 2024

Mad-max beyond single-node: Enabling large machine learning model acceleration on distributed systems

Training and deploying large-scale machine learning models is time-consuming, requires significant distributed computing infrastructures, and incurs high operational costs…

June 29th, 2024

Mad-max beyond single-node: Enabling large machine learning model acceleration on distributed systems

Training and deploying large-scale machine learning models is time-consuming, requires significant distributed computing infrastructures, and incurs high operational costs…

June 29th, 2024

Is flash attention stable?

Training large-scale machine learning models poses distinct system challenges, given both the size and complexity of today's workloads. Recently, many organizations…

May 5th, 2024

Is flash attention stable?

Training large-scale machine learning models poses distinct system challenges, given both the size and complexity of today's workloads. Recently, many organizations…

May 5th, 2024

Is flash attention stable?

Training large-scale machine learning models poses distinct system challenges, given both the size and complexity of today's workloads. Recently, many organizations…

May 5th, 2024

Is flash attention stable?

Training large-scale machine learning models poses distinct system challenges, given both the size and complexity of today's workloads. Recently, many organizations…

May 5th, 2024

Generative ai beyond llms: System implications of multi-modal generation

As the development of large-scale Generative AI models evolve beyond text (1D) generation to include image (2D) and video (3D) generation, processing spatial and temporal…

May 5th, 2024

Generative ai beyond llms: System implications of multi-modal generation

As the development of large-scale Generative AI models evolve beyond text (1D) generation to include image (2D) and video (3D) generation, processing spatial and temporal…

May 5th, 2024

Generative ai beyond llms: System implications of multi-modal generation

As the development of large-scale Generative AI models evolve beyond text (1D) generation to include image (2D) and video (3D) generation, processing spatial and temporal…

May 5th, 2024

Generative ai beyond llms: System implications of multi-modal generation

As the development of large-scale Generative AI models evolve beyond text (1D) generation to include image (2D) and video (3D) generation, processing spatial and temporal…

May 5th, 2024

Towards Fine-Tunable Quantized Large Language Models with Error Correction through Low-Rank Adaptation

We introduce a method that dramatically reduces fine-tuning VRAM requirements and rectifies quantization errors in quantized Large Language Models…

June 13th, 2023