Search Results for author:
Found 13 papers, 1 papers with code
INT-FlashAttention: Enabling Flash Attention for INT8 Quantization
As the foundation of large language models (LLMs), self-attention module faces the challenge of quadratic time and memory complexity with respect to sequence length.
MCNS: Mining Causal Natural Structures Inside Time Series via A Novel Internal Causality Scheme
To address these challenges, we propose a novel framework called Mining Causal Natural Structure (MCNS), which is automatic and domain-agnostic and helps to find the causal natural structures inside time series via the internal causality scheme.
CMLCompiler: A Unified Compiler for Classical Machine Learning
Without a unified framework, the hybrid deployments of deep learning (DL) and CML also suffer from severe performance and portability issues.
FuncFooler: A Practical Black-box Attack Against Learning-based Binary Code Similarity Detection Methods
The binary code similarity detection (BCSD) method measures the similarity of two binary executable codes.
OpenClinicalAI: enabling AI to diagnose diseases in real-world clinical settings
In the real-world clinical setting, OpenClinicalAI significantly outperforms the state-of-the-art AI system.
Pinpointing the Memory Behaviors of DNN Training
The training of deep neural networks (DNNs) is usually memory-hungry due to the limited device memory capacity of DNN accelerators.
HPC AI500: Representative, Repeatable and Simple HPC AI Benchmarking
Recent years witness a trend of applying large-scale distributed deep learning algorithms (HPC AI) in both business and scientific computing areas, whose goal is to speed up the training time to achieve a state-of-the-art quality.
Image Classification
Performance
AIBench Scenario: Scenario-distilling AI Benchmarking
We formalize a real-world application scenario as a Directed Acyclic Graph-based model and propose the rules to distill it into a permutation of essential AI and non-AI tasks, which we call a scenario benchmark.
AIBench Training: Balanced Industry-Standard AI Training Benchmarking
We use real-world benchmarks to cover the factors space that impacts the learning dynamics to the most considerable extent.
AIBench: An Agile Domain-specific Benchmarking Methodology and an AI Benchmark Suite
An end-to-end benchmark is a distillation of the essential attributes of an industry-scale application.
The Pitfall of Evaluating Performance on Emerging AI Accelerators
To reveal this pitfall, we evaluates several frequently-used optimizations on a typical AI accelerator and quantifies their impact on accuracy and throughout under representative DL inference workloads.
HPC AI500: A Benchmark Suite for HPC AI Systems
In this paper, we propose HPC AI500 --- a benchmark suite for evaluating HPC systems that running scientific DL workloads.
A Semantic-based Medical Image Fusion Approach
Medical image fusion is a promising approach to providing overall information from medical images of different modalities.
