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Found 22 papers, 2 papers with code
AIGCBench: Comprehensive Evaluation of Image-to-Video Content Generated by AI
To establish a unified evaluation framework for video generation tasks, our benchmark includes 11 metrics spanning four dimensions to assess algorithm performance.
AGIBench: A Multi-granularity, Multimodal, Human-referenced, Auto-scoring Benchmark for Large Language Models
Instead of a collection of blended questions, AGIBench focuses on three typical ability branches and adopts a four-tuple <ability branch, knowledge, difficulty, modal> to label the attributes of each question.
Does AI for science need another ImageNet Or totally different benchmarks? A case study of machine learning force fields
This suite of metrics has demonstrated a better ability to assess a model's performance in real-world scientific applications, in contrast to traditional AI benchmarking methodologies.
IterLara: A Turing Complete Algebra for Big Data, AI, Scientific Computing, and Database
The study of \textsc{Lara}'s expressive ability reports that it can represent relational algebra and most linear algebra operations.
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.
Quality at the Tail of Machine Learning Inference
The study unveils a counterintuitive revelation: deep learning inference quality exhibits fluctuations due to inference time.
ToL: A Tensor of List-Based Unified Computation Model
This article presents a unified computation model with generalized expression ability and a concise set of primitive operators for programming high-level algorithms.
Heterologous Normalization
Specifically, it calculates the mean like Batch Normalization to maintain the advantage of Batch Normalization.
Shift-and-Balance Attention
The attention branch is gated using the Sigmoid function and multiplied by the feature map's trunk branch.
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
Finet: Using Fine-grained Batch Normalization to Train Light-weight Neural Networks
To build light-weight network, we propose a new normalization, Fine-grained Batch Normalization (FBN).
Comparison and Benchmarking of AI Models and Frameworks on Mobile Devices
Due to increasing amounts of data and compute resources, deep learning achieves many successes in various domains.
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.
Extended Batch Normalization
To alleviate the problem caused by small batch size, extended batch normalization computes the standard deviation along the (N, C, H, W) dimensions, thus enlarges the number of samples from which the standard deviation is computed.
Ranked #62 on
Image Classification
on STL-10
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.
BenchCouncil's View on Benchmarking AI and Other Emerging Workloads
This paper outlines BenchCouncil's view on the challenges, rules, and vision of benchmarking modern workloads like Big Data, AI or machine learning, and Internet Services.
AIBench: An Industry Standard Internet Service AI Benchmark Suite
On the basis of the AIBench framework, abstracting the real-world data sets and workloads from one of the top e-commerce providers, we design and implement the first end-to-end Internet service AI benchmark, which contains the primary modules in the critical paths of an industry scale application and is scalable to deploy on different cluster scales.
Edge AIBench: Towards Comprehensive End-to-end Edge Computing Benchmarking
In edge computing scenarios, the distribution of data and collaboration of workloads on different layers are serious concerns for performance, privacy, and security issues.
Performance Distributed, Parallel, and Cluster Computing
LoadCNN: A Low Training Cost Deep Learning Model for Day-Ahead Individual Residential Load Forecasting
As a consequence, the deep learning methods are difficult to be popularized and applied in the real smart grid environment.
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.
BigDataBench: A Scalable and Unified Big Data and AI Benchmark Suite
On the basis of our previous work that identifies eight data motifs taking up most of the run time of a wide variety of big data and AI workloads, we propose a scalable benchmarking methodology that uses the combination of one or more data motifs---to represent diversity of big data and AI workloads.
