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Achieving Peak Performance for Large Language Models: A Systematic Review
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[["Zhyar Rzgar K Rostam", "Buda Health Center"], ["S\u00e1ndor Sz\u00e9n\u00e1si", "Buda Health Center"], ["G\u00e1bor Kert\u00e9sz", "Institute for Computer Science and Control"]]
|
IEEE Access
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In recent years, large language models (LLMs) have achieved remarkable\nsuccess in natural language processing (NLP). LLMs require an extreme amount of\nparameters to attain high performance. As models grow into the\ntrillion-parameter range, computational and memory costs increase\nsignificantly. This makes it difficult for many researchers to access the\nresources needed to train or apply these models. Optimizing LLM performance\ninvolves two main approaches: fine-tuning pre-trained models for specific tasks\nto achieve state-of-the-art performance, and reducing costs or improving\ntraining time while maintaining similar performance. This paper presents a\nsystematic literature review (SLR) following the Preferred Reporting Items for\nSystematic Reviews and Meta-Analyses (PRISMA) statement. We reviewed 65\npublications out of 983 from 2017 to December 2023, retrieved from 5 databases.\nThe study presents methods to optimize and accelerate LLMs while achieving\ncutting-edge results without sacrificing accuracy. We begin with an overview of\nthe development of language modeling, followed by a detailed explanation of\ncommonly used frameworks and libraries, and a taxonomy for improving and\nspeeding up LLMs based on three classes: LLM training, LLM inference, and\nsystem serving. We then delve into recent optimization and acceleration\nstrategies such as training optimization, hardware optimization, scalability\nand reliability, accompanied by the taxonomy and categorization of these\nstrategies. Finally, we provide an in-depth comparison of each class and\nstrategy, with two case studies on optimizing model training and enhancing\ninference efficiency. These case studies showcase practical approaches to\naddress LLM resource limitations while maintaining performance.\n
| true
| 4
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The abstract addresses algorithmic efficiency through training optimization and pruning, architectural design via LLM architecture trends, and hardware optimization with accelerator-aware methods. While it lacks focus on data processing selection, it provides a comprehensive review of methods relevant to making AI more accessible and efficient for trainers and deployers.
|
{"algorithmic_efficiency": "Discusses training optimization and pruning strategies", "architectural_design": "Reviews architectural approaches in LLMs for efficiency", "data_processing_selection": "Does not address data selection or preprocessing", "hardware_optimization": "Explicitly covers hardware optimization strategies"}
|
{"references": [], "citations": ["achieving_peak_performance_for_large_language_models_a_systematic_review"]}
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{"umap_x": 2.6593964099884033, "umap_y": 4.380886077880859}
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achieving_peak_performance_for_large_language_models_a_systematic_review
| null | |
MizAR 60 for Mizar 50
| 2,023
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[["Ashish Vaswani", ""], ["Noam Shazeer", ""], ["Niki Parmar", ""], ["Jakob Uszkoreit", ""], ["Llion Jones", ""], ["Aidan N. Gomez", ""], ["\u0141ukasz Kaiser", ""], ["Illia Polosukhin", ""]]
|
Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
|
As a present to Mizar on its 50th anniversary, we develop an AI/TP system that automatically proves about 60% of the Mizar theorems in the hammer setting. We also automatically prove 75% of the Mizar theorems when the automated provers are helped by using only the premises used in the human-written Mizar proofs. We describe the methods and large-scale experiments leading to these results. This includes in particular the E and Vampire provers, their ENIGMA and Deepire learning modifications, a number of learning-based premise selection methods, and the incremental loop that interleaves growing a corpus of millions of ATP proofs with training increasingly strong AI/TP systems on them. We also present a selection of Mizar problems that were proved automatically.
| false
| 3
|
The paper addresses algorithmic efficiency through learning-based premise selection and data processing via active learning and corpus growth. However, it does not discuss architectural design, hardware co-design, or explicit optimization for deployment or training efficiency. While it touches on efficiency in automated reasoning, it lacks direct relevance to AI models for trainers or deployers in terms of computational accessibility or hardware integration.
|
{"algorithmic_efficiency": "Learning-based premise selection improves proof efficiency", "architectural_design": "No novel architecture proposed; relies on existing provers", "data_processing_selection": "Active learning and premise selection for proof generation", "hardware_optimization": "Not addressed; focuses on software-based theorem proving"}
|
{"references": ["achieving_peak_performance_for_large_language_models_a_systematic_review", "dfx_a_lowlatency_multifpga_appliance_for_accelerating_transformerbased_text_generation", "lightseq2_accelerated_training_for_transformerbased_models_on_gpus", "easy_and_efficient_transformer_scalable_inference_solution_for_large_nlp_model", "deepspeed_inference_enabling_efficient_inference_of_transformer_models_at_unprecedented_scale", "alphatuning_quantizationaware_parameterefficient_adaptation_of_largescale_pretrained_language_models", "efficient_llms_training_and_inference_an_introduction", "norm_tweaking_highperformance_lowbit_quantization_of_large_language_models"], "citations": []}
|
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{"umap_x": 0.9614489078521729, "umap_y": 1.2693313360214233}
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mizar_60_for_mizar_50
| null | |
Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey
| 2,023
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[["Bonan Min", "Amazon (United States)"], ["Hayley Ross", "Harvard University Press"], ["Elior Sulem", "California University of Pennsylvania"], ["Amir Pouran Ben Veyseh", "University of Oregon"], ["Thien Huu Nguyen", "University of Oregon"], ["Oscar Sainz", "University of the Basque Country"], ["Eneko Agirre", "University of the Basque Country"], ["Ilana Heintz", "Synaptic Research (United States)"], ["Dan Roth", "California University of Pennsylvania"]]
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ACM Computing Surveys
|
Large, pre-trained language models (PLMs) such as BERT and GPT have drastically changed the Natural Language Processing (NLP) field. For numerous NLP tasks, approaches leveraging PLMs have achieved state-of-the-art performance. The key idea is to learn a generic, latent representation of language from a generic task once, then share it across disparate NLP tasks. Language modeling serves as the generic task, one with abundant self-supervised text available for extensive training. This article presents the key fundamental concepts of PLM architectures and a comprehensive view of the shift to PLM-driven NLP techniques. It surveys work applying the pre-training then fine-tuning, prompting, and text generation approaches. In addition, it discusses PLM limitations and suggested directions for future research.
| false
| 2
|
The abstract focuses on PLM architectures and training paradigms but does not discuss algorithmic efficiency, architectural optimizations for speed/memory, data reduction methods, or hardware co-design. It provides a general survey of NLP advancements rather than addressing efficiency or accessibility for trainers or deployers.
|
{"algorithmic_efficiency": "Not addressed", "architectural_design": "Not addressed", "data_processing_selection": "Not addressed", "hardware_optimization": "Not addressed"}
|
{"references": ["achieving_peak_performance_for_large_language_models_a_systematic_review", "a_survey_of_text_classification_with_transformers_how_wide_how_large_how_long_how_accurate_how_expensive_how_safe"], "citations": ["easy_and_efficient_transformer_scalable_inference_solution_for_large_nlp_model"]}
|
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|
{"umap_x": 0.470488041639328, "umap_y": 2.85550856590271}
|
recent_advances_in_natural_language_processing_via_large_pretrained_language_models_a_survey
| null | |
Talking about Large Language Models
| 2,024
|
[["Murray Shanahan", "Imperial College London"]]
|
Communications of the ACM
|
Interacting with a contemporary LLM-based conversational agent can create an illusion of being in the presence of a thinking creature. Yet, in their very nature, such systems are fundamentally not like us.
| false
| 1
|
The abstract discusses the phenomenological aspect of LLM interactions but does not touch on algorithmic efficiency, architectural design, data processing, or hardware optimization. It lacks technical details relevant to making AI more efficient or accessible for trainers and deployers.
|
{"algorithmic_efficiency": "not addressed", "architectural_design": "not addressed", "data_processing_selection": "not addressed", "hardware_optimization": "not addressed"}
|
{"references": ["achieving_peak_performance_for_large_language_models_a_systematic_review"], "citations": []}
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{"umap_x": -0.6382922530174255, "umap_y": 2.345301389694214}
|
talking_about_large_language_models
|
https://dl.acm.org/doi/pdf/10.1145/3624724
| |
DeepSpeed- Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale
| 2,022
|
[["Reza Yazdani Aminabadi", "Microsoft (United States)"], ["Samyam Rajbhandari", "Microsoft (United States)"], ["Ammar Ahmad Awan", "Microsoft (United States)"], ["Cheng Li", "Microsoft (United States)"], ["Canbing Li", "Microsoft (United States)"], ["Elton Zheng", "Microsoft (United States)"], ["Olatunji Ruwase", "Microsoft (United States)"], ["Shaden Smith", "Microsoft (United States)"], ["Minjia Zhang", "Microsoft (United States)"], ["Jeff Rasley", "Microsoft (United States)"], ["Yuxiong He", "Microsoft (United States)"]]
|
The landscape of transformer model inference is increasingly diverse in model size, model characteristics, latency and throughput requirements, hardware requirements, etc. With such diversity, designing a versatile inference system is challenging. DeepSpeed-Inference addresses these challenges by (1) a multi-GPU inference solution to minimize latency while maximizing throughput for both dense and sparse transformers when the model fits in aggregate GPU memory, and (2) a heterogeneous inference solution that leverages CPU/NVMe/GPU memory to enable high-throughput inference for models larger than aggregate GPU memory. DeepSpeed-Inference reduces latency by 6.4× and increases throughput by 1.5 ×over the state-of-the-art. It enables trillion parameter scale inference under real-time latency constraints by leveraging hundreds of GPUs, an unprecedented scale for inference. It can inference 25 ×larger models than with GPU-only solutions, while delivering a high throughput of 84 TFLOPS (over 50% of A6000 peak).
| true
| 4
|
The abstract addresses algorithmic efficiency through sparse transformers and memory optimization, architectural design via multi-GPU and heterogeneous systems, and hardware optimization through CPU/NVMe/GPU co-design. It directly supports the research question by advancing efficient, scalable inference for large models under real-world deployment constraints.
|
{"algorithmic_efficiency": "Focuses on sparse transformers and memory-efficient inference", "architectural_design": "Provides multi-GPU and heterogeneous architecture for scalable inference", "data_processing_selection": "NONE", "hardware_optimization": "Leverages CPU/NVMe/GPU co-design for hardware-aware inference"}
|
{"references": ["achieving_peak_performance_for_large_language_models_a_systematic_review", "efficient_llms_training_and_inference_an_introduction", "norm_tweaking_highperformance_lowbit_quantization_of_large_language_models"], "citations": ["deepspeed_inference_enabling_efficient_inference_of_transformer_models_at_unprecedented_scale"]}
|
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|
{"umap_x": 3.242175579071045, "umap_y": 5.023370742797852}
|
deepspeed_inference_enabling_efficient_inference_of_transformer_models_at_unprecedented_scale
| null | ||
Language Model Behavior: A Comprehensive Survey
| 2,023
|
[["Tyler A. Chang", "University of California, San Diego"], ["Benjamin Bergen", "University of California, San Diego"]]
|
Computational Linguistics
|
Abstract Transformer language models have received widespread public attention, yet their generated text is often surprising even to NLP researchers. In this survey, we discuss over 250 recent studies of English language model behavior before task-specific fine-tuning. Language models possess basic capabilities in syntax, semantics, pragmatics, world knowledge, and reasoning, but these capabilities are sensitive to specific inputs and surface features. Despite dramatic increases in generated text quality as models scale to hundreds of billions of parameters, the models are still prone to unfactual responses, commonsense errors, memorized text, and social biases. Many of these weaknesses can be framed as over-generalizations or under-generalizations of learned patterns in text. We synthesize recent results to highlight what is currently known about large language model capabilities, thus providing a resource for applied work and for research in adjacent fields that use language models.
| false
| 1
|
The abstract does not discuss algorithmic efficiency, architectural design, data processing/selection, or hardware optimization. It focuses broadly on language model behavior, capabilities, and limitations rather than efficiency or accessibility improvements for trainers and deployers.
|
{"algorithmic_efficiency": "not addressed", "architectural_design": "not addressed", "data_processing_selection": "not addressed", "hardware_optimization": "not addressed"}
|
{"references": ["achieving_peak_performance_for_large_language_models_a_systematic_review"], "citations": ["easy_and_efficient_transformer_scalable_inference_solution_for_large_nlp_model"]}
|
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|
{"umap_x": 0.15042969584465027, "umap_y": 2.475184202194214}
|
language_model_behavior_a_comprehensive_survey
|
https://direct.mit.edu/coli/article-pdf/doi/10.1162/coli_a_00492/2177312/coli_a_00492.pdf
| |
Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training
| 2,023
|
[["Shenggui Li", "Singapore Institute of Technology"], ["Hongxin Liu", ""], ["Zhengda Bian", ""], ["Jiarui Fang", ""], ["Haichen Huang", ""], ["Yuliang Liu", ""], ["Boxiang Wang", "Singapore Institute of Technology"], ["Yang You", "National University of Singapore"]]
|
The success of Transformer models has pushed the deep learning model scale to billions of parameters, but the memory limitation of a single GPU has led to an urgent need for training on multi-GPU clusters. However, the best practice for choosing the optimal parallel strategy is still lacking, as it requires domain expertise in both deep learning and parallel computing. The Colossal-AI system addressed the above challenge by introducing a unified interface to scale your sequential code of model training to distributed environments. It supports parallel training methods such as data, pipeline, tensor, and sequence parallelism and is integrated with heterogeneous training and zero redundancy optimizer. Compared to the baseline system, Colossal-AI can achieve up to 2.76 times training speedup on large-scale models.
| false
| 3
|
The abstract does not focus on algorithmic efficiency, architectural design, or data processing/selective strategies. It addresses hardware scalability through parallelism in distributed training, indirectly relating to hardware optimization via multi-GPU and heterogeneous system support. While relevant to large-scale deployment, it does not improve efficiency at the algorithmic or data level or offer lightweight models for accessibility.
|
{"algorithmic_efficiency": "Not addressed", "architectural_design": "Not directly addressed", "data_processing_selection": "Not addressed", "hardware_optimization": "Indirectly considered via hardware-aware parallelism"}
|
{"references": ["achieving_peak_performance_for_large_language_models_a_systematic_review", "efficient_llms_training_and_inference_an_introduction"], "citations": []}
|
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|
{"umap_x": 3.6683053970336914, "umap_y": 4.17811918258667}
|
colossalai_a_unified_deep_learning_system_for_largescale_parallel_training
| null | ||
ByteTransformer: A High-Performance Transformer Boosted for Variable-Length Inputs
| 2,023
|
[["Yujia Zhai", "University of California, Riverside"], ["Chengquan Jiang", ""], ["Leyuan Wang", ""], ["Xiaoying Jia", ""], ["Shang Zhang", "Nvidia (United States)"], ["Zizhong Chen", "University of California, Riverside"], ["Xin Liu", ""], ["Yibo Zhu", ""]]
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Transformers have become keystone models in natural language processing over the past decade. They have achieved great popularity in deep learning applications, but the increasing sizes of the parameter spaces required by transformer models generate a commensurate need to accelerate performance. Natural language processing problems are also routinely faced with variable-length sequences, as word counts commonly vary among sentences. Existing deep learning frameworks pad variable-length sequences to a maximal length, which adds significant memory and computational overhead. In this paper, we present ByteTransformer, a high-performance transformer boosted for variable-length inputs. We propose a padding-free algorithm that liberates the entire transformer from redundant computations on zero padded tokens. In addition to algorithmic-level optimization, we provide architecture-aware optimizations for transformer functional modules, especially the performance-critical algorithm Multi-Head Attention (MHA). Experimental results on an NVIDIA A100 GPU with variable-length sequence inputs validate that our fused MHA outperforms PyTorch by 6.13x. The end-to-end performance of ByteTransformer for a forward BERT transformer surpasses state-of-the-art transformer frameworks, such as PyTorch JIT, TensorFlow XLA, Tencent TurboTransformer, Microsoft DeepSpeed-Inference and NVIDIA FasterTransformer, by 87%, 131%, 138%, 74% and 55%, respectively. We also demonstrate the general applicability of our optimization methods to other BERT-like models, including ALBERT, DistilBERT, and DeBERTa.
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The paper addresses algorithmic efficiency through a padding-free approach that eliminates redundant computations on padded tokens, and enhances architectural design via optimized Multi-Head Attention modules. These improvements directly reduce computational and memory overhead in variable-length sequence processing, aligning with the research question's focus on efficiency and model design in AI deployment. The work is highly relevant to making transformers more efficient and accessible for practitioners dealing with real-world, variable-length inputs.
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{"algorithmic_efficiency": "Padding-free computation reduces redundant operations", "architectural_design": "Architecture-aware MHA optimizations improve performance", "data_processing_selection": "NONE", "hardware_optimization": "NONE"}
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{"references": ["achieving_peak_performance_for_large_language_models_a_systematic_review"], "citations": []}
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|
{"umap_x": 1.7971622943878174, "umap_y": 4.869078636169434}
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bytetransformer_a_highperformance_transformer_boosted_for_variablelength_inputs
| null | ||
Sentiment Analysis with Neural Models for Hungarian
| 2,023
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[["L\u00e1szl\u00f3 J\u00e1nos Laki", "Hungarian Research Centre for Linguistics"], ["Zijian Gy\u0151z\u0151 Yang", ""]]
|
Acta Polytechnica Hungarica
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Sentiment analysis is a powerful tool to gain insight into the emotional polarity of opinionated texts.Computerized applications can contribute to the establishment of nextgeneration models that can provide us with data of unprecedented quantity and quality.However, these models often require substantial amount of resources in order to meet the desired performance expectations.Therefore, numerous research efforts are targeted to achieve high-quality results while lowering the resource needs by improving the structure and function of the models used.From a cognitive perspective, it is important to understand the mental state of users when they engage in activities that potentially reflect their feelings and emotions.With the emergence of the widespread use of digital solutions, users post opinionated texts on social media, which can be used as a valuable source to detect their underlying sentiments.Therefore, these platforms offer an unparalleled opportunity to perform sentiment analysis.In recent years, natural language processing tasks, like sentiment analysis, can be solved with high performance, if a pre-trained language model is fine-tuned.Herein we present the first neural transformer-based sentiment analysis model for Hungarian, which achieved state-of-the-art performance.Several limitation factors can occur during fine-tuning, such as the lack of training corpora with appropriate size or the complete absence of usable training material.In our experiment, we use data augmentation methods, specifically machine translation and cross-lingual transfer, to increase the size of our training corpora.Here, we demonstrate our experimentation with 9 different language models.Our work provides evidence for the increased efficiency of the trained models if translation text is added to the training corpora.Furthermore, using the augmentation technique, we could further increase the performance of our models.Consequently, our findings represent an important milestone in the advancement of sentence-level and aspectbased sentiment analysis in the Hungarian language.
| false
| 3
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The paper addresses algorithmic efficiency through data augmentation to reduce training resource needs and improves model performance. While it uses advanced architectural designs (transformers) and data processing (cross-lingual transfer), it does not discuss hardware co-design or optimization. It is relevant to AI efficiency and data selection in NLP, particularly in low-resource language settings, but lacks focus on hardware or lightweight model design.
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{"algorithmic_efficiency": "Data augmentation improves model efficiency", "architectural_design": "Uses neural transformer architecture for Hungarian sentiment analysis", "data_processing_selection": "Applies machine translation and cross-lingual transfer to expand training data", "hardware_optimization": "NONE"}
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{"references": ["achieving_peak_performance_for_large_language_models_a_systematic_review"], "citations": []}
|
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|
{"umap_x": -0.4204283058643341, "umap_y": 4.553020000457764}
|
sentiment_analysis_with_neural_models_for_hungarian
|
https://doi.org/10.12700/aph.20.5.2023.5.8
| |
ResearchRabbit (product review)
| 2,023
|
[["Victoria Cole", "University of Ottawa"], ["Mish Boutet", "University of Ottawa"]]
| "Journal of the Canadian Health Libraries Association / Journal de l Association de bilbiothèques d(...TRUNCATED)
| "ResearchRabbit is a scholarly publication discovery tool supported by artificial intelligence (AI).(...TRUNCATED)
| false
| 1
| "The abstract describes a scholarly discovery tool that uses AI for recommendation generation based (...TRUNCATED)
| "{\"algorithmic_efficiency\": \"Not applicable\", \"architectural_design\": \"Not applicable\", \"da(...TRUNCATED)
| "{\"references\": [\"achieving_peak_performance_for_large_language_models_a_systematic_review\"], \"(...TRUNCATED)
| "{\"embedding\": [-0.024287216365337372, 0.010205757804214954, -0.012635764665901661, -0.02005367912(...TRUNCATED)
|
{"umap_x": 0.004384573083370924, "umap_y": 3.5795931816101074}
|
researchrabbit_product_review
|
https://journals.library.ualberta.ca/jchla/index.php/jchla/article/download/29699/21872
|
End of preview. Expand
in Data Studio
ai_efficiency_selection
About This Dataset
This dataset was created using 🏖️ Tidepool Research: LLM-Enabled Literature Review, an interactive tool for building comprehensive literature review corpora through systematic discovery, evaluation, and organization of academic papers.
The app uses language models to score paper relevance and citation networks to discover related work. You can use the app to create your own literature review datasets or explore this corpus interactively.
🔗 Try the app: https://huggingface.co/spaces/hfmlsoc/Lit_Review_with_LMs
Research Question
Papers on making artificial intelligence more efficient and accessible to trainers and deployers, taking into account algorithms, architecture, data processing and selection, and the role of hardware and hardware optimization
Configuration
- Academic API: OpenAlex
- Cutoff Year: 2022
- Fetch Mode: References + Citations
- LLM Model: Qwen/Qwen3-4B-Instruct-2507
- LLM Mode: HF Inference Endpoint
- Endpoint: qwen3-next-80b-a3b-instruct-tpx
Research Aspects
- algorithmic_efficiency: Focus on compression, pruning, quantization, or lightweight models to reduce computational cost during training/inference. Examples: knowledge distillation, sparse networks, low-precision training.
- architectural_design: Novel model architectures optimized for speed, memory, or energy without sacrificing performance. Examples: mobile-optimized CNNs, transformer variants like TinyBERT, modular designs.
- data_processing_selection: Strategies to reduce data burden via selection, synthesis, or preprocessing. Examples: active learning, data distillation, synthetic data generation, efficient labeling pipelines.
- hardware_optimization: Co-design of software with hardware (e.g., accelerators, edge devices) for efficiency. Examples: FPGA/TPU-aware models, on-device inference frameworks, power-aware scheduling.
Dataset Information
This dataset contains papers collected and analyzed for a literature review. Papers are scored for relevance to the research question using LLM-based analysis.
Note: Dataset metadata (research question, configuration, etc.) is stored in metadata.json for efficient browsing without downloading the full dataset.
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